System

Abstract

A system is provided.SOLUTION: A system comprising: means for centrally collecting personal health data; means for preprocessing the health data; means for aggregating the preprocessed data; means for analyzing the aggregated data in real-time; means for generating a customized health care plan; and means for notifying a user of the health care plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern healthcare, personal health management requires handling a wide range of data. However, the collection, management, and analysis of health data are distributed, making effective communication between medical professionals and patients difficult. It is also difficult to quickly provide customized health management plans tailored to individual health needs. Therefore, there is a need for a system that can centrally manage personal health data, analyze it in real time, and provide customized health management plans. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including a means for centrally collecting personal health data, a means for preprocessing the health data, a means for integrating the preprocessed data, a means for analyzing the integrated data in real time, a means for generating a customized health management plan, and a means for notifying a user of the health management plan. Furthermore, when the health data includes text data, audio data, image data, and video data, each function is specifically realized by a means for real-time analysis including analysis using a generative model, a means for collecting user feedback, and a collection means using a smartphone or a wearable device.

[0006] "Personal health data" refers to any type of data related to an individual's health status, including text data, audio data, image data, and video data.

[0007] "Centralized collection method" refers to a method or device for securely and centrally collecting personal health data from different devices or sources.

[0008] "Pre-processing means" refers to a method or device for converting collected health data into a unified and analyzable format.

[0009] "Means for integrating" refers to a method or device for combining pre-processed health data in various formats into a single dataset.

[0010] "Real-time analytical means" refers to methods or devices used to obtain analytical results immediately after data is collected or with very little delay.

[0011] A "generative model" refers to an algorithm that uses machine learning and artificial intelligence techniques to extract specific patterns from data and make predictions or classifications.

[0012] A "health management plan" refers to a personalized plan that suggests specific exercise, diet, sleep, and other actions to improve or maintain a user's health.

[0013] The "means for notifying the user" refers to a method or apparatus for transmitting the generated health management plan and analysis results in an appropriate format to a device used by the user.

[0014] "Means for collecting feedback" refers to a method or device for efficiently collecting execution results and opinions provided by users and reflecting them in the system.

[0015] "Smartphone or wearable device" refers to a digital device that is carried or worn by an individual and used daily, and which plays a role in collecting and notifying health data. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention provides a system for centrally collecting, pre-processing, integrating, and analyzing personal health data in real time to provide a customized health management plan. Specific embodiments of the system are described in detail below.

[0038] 1. Collection of User Data

[0039] Users use smartphones or wearable devices to input their daily health data, which includes text data (health diaries and survey responses), audio data (voice memos), image data (scanned health records), and video data (exercise videos, etc.).

[0040] 2. Data Preprocessing

[0041] The device preprocesses the health data sent by the user, specifically adjusting the resolution of image data, converting audio data to text, and cleaning and converting text data into a unified format. This preprocessing makes the data suitable for subsequent integration and analysis.

[0042] 3. Data integration

[0043] The server then integrates the pre-processed data, combining data from different formats into a single unified dataset, allowing individual users' health data to be managed centrally.

[0044] 4. AI-powered analysis

[0045] The server analyzes the integrated health data, using a generative model (e.g., a machine learning algorithm) to assess the user's health status from a variety of data. For example, it determines stress levels and lack of exercise from daily health logs.

[0046] 5. Generate a health management plan

[0047] The server generates a customized health management plan based on the analysis results, which includes specific dietary, exercise, and sleep recommendations, allowing users to receive advice optimized for their health status.

[0048] 6. Notice to Users

[0049] The server then notifies the user of the generated health management plan via their smartphone or wearable device. The notification format is tailored to the user's preferences and the characteristics of the device, and may include push notifications for the mobile app or alerts for the wearable device.

[0050] 7. Gathering Feedback

[0051] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0052] Specific examples

[0053] For example, if a user records their diet on a smartphone app and measures their exercise volume with a wearable device, this data is preprocessed on the device and sent to a server. The server then integrates this data and evaluates the user's health using an AI model. Based on the evaluation results, a specific plan is generated, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, allowing future plans to be further personalized.

[0054] The above is a specific embodiment of the present invention, which makes it possible to effectively and efficiently support the health management of a user.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] Users use smartphones or wearable devices to input or collect health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of the day's meals into a smartphone app and measure the number of steps taken and heart rate with a smartwatch.

[0058] Step 2:

[0059] The device receives the collected data and performs preprocessing. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes the content to extract important indicators such as sleep time and exercise volume.

[0060] Step 3:

[0061] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0062] Step 4:

[0063] The server analyzes the integrated data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it may determine that the user is lacking exercise based on data from the past week.

[0064] Step 5:

[0065] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0066] Step 6:

[0067] The server notifies the user of the generated health management plan. The plan is communicated to the user using push notifications on the smartphone app, emails, and alerts on wearable devices. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes."

[0068] Step 7:

[0069] The user carries out the health management plan they receive. They then input their results and impressions into the smartphone app as feedback. For example, they can record in the app that they "walked for 30 minutes today" and enter comments about their physical condition and impressions during the walk.

[0070] Step 8:

[0071] The server collects feedback from users and stores it in a database. This feedback data is used to improve the accuracy of the next health management plan. For example, if a user gives feedback that "30 minutes of walking is too strenuous," the server will suggest a 15-minute walk next time.

[0072] The above processing steps realize a system that efficiently manages a user's health data and provides an optimal health management plan according to the individual health condition.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] In modern society, personal health management is extremely important, and there is a demand for providing appropriate health plans based on various data. However, existing health management systems have difficulty efficiently collecting, integrating, and analyzing data from multiple data sources, making it difficult to provide customized plans tailored to users in real time. Furthermore, the lack of a function to improve plans based on user feedback has limited the effectiveness of long-term health management.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes means for centrally collecting personal health data, means for preprocessing the health data, means for integrating the preprocessed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for notifying the user of the health management plan, and means for collecting feedback from the user and reflecting the feedback in the next plan, thereby making it possible to provide an optimal health management plan for each individual user and to continuously improve it.

[0078] "Personal health data" refers to data about a user's daily life, such as their heart rate, amount of exercise, diet, and stress level.

[0079] "Preprocessing" refers to the process of converting raw data collected from users into a format that is easier to analyze, and specifically includes adjusting resolution, converting speech to text, and cleaning the data.

[0080] "Integration" refers to the process of combining datasets of different formats into one unified dataset.

[0081] "Real-time analysis" refers to the process of instantly analyzing collected data and reflecting the results immediately.

[0082] "Customized Health Plan" refers to a health plan that includes specific recommended dietary, exercise, sleep, and other actions based on a user's individual data.

[0083] "Feedback" refers to information that the user re-enters into the system regarding the results and impressions of the health management plan that he or she has implemented.

[0084] A "machine learning algorithm" refers to a set of computational techniques that allow a computer to analyze data, recognize patterns, and make predictions.

[0085] The present invention is a system that centrally collects, pre-processes, integrates, and analyzes personal health data in real time to provide customized health management plans.

[0086] 1. Collection of User Data

[0087] Users input health data using smartphones or wearable devices. This data includes text data, voice data, image data, and video data. For example, a user might record "Today's breakfast is vegetable salad and an omelet" in a smartphone app, and the wearable device measures the amount of exercise. This data is sent to the terminal in real time.

[0088] 2. Data Preprocessing

[0089] The device preprocesses the received health data. For example, it uses image processing software (e.g., OpenCV) to adjust the resolution of the photos and remove noise. It also converts the audio data into text using a speech recognition system (e.g., Google Speech-to-Text API), and uses regular expressions to remove unnecessary characters and spaces. This converts the data into a format suitable for analysis.

[0090] 3. Data integration

[0091] The server receives the preprocessed data and stores it in a database system (e.g., MySQL). The server then combines data sets of different formats into a unified data set. This process uses data mapping, for example, to link text data, image data, and audio data by user ID.

[0092] 4. AI-powered analysis

[0093] The server analyzes the integrated data using machine learning algorithms (e.g., TensorFlow or PyTorch). The server compares the user's past data with their current data to recognize patterns. For example, it analyzes past health logs to assess lack of exercise and stress levels. In this process, it utilizes generative AI models to assess the user's health status in real time.

[0094] 5. Generate a health management plan

[0095] The server generates a customized health management plan based on the analysis results. For example, based on the analysis results of "lack of exercise" and "insufficient vitamin intake," the server creates a specific action plan such as "recommended yoga three times a week" or "eat fruit every day." It also advises the user on specific meal menus and recommended exercise amounts.

[0096] 6. Notice to Users

[0097] The server notifies the user of the generated health management plan via a smartphone app or wearable device. For example, it can send a push notification to the smartphone app to inform the user of the new health plan. It can also notify the wearable device via vibration or an alert.

[0098] 7. Gathering Feedback

[0099] Users input the results and impressions of the health management plan they have completed as feedback into the smartphone app. This feedback data is sent back to the server, which analyzes it and uses it to improve the next health management plan. For example, more personalized suggestions can be made based on feedback such as "I like yoga, so I want to continue."

[0100] Specific examples

[0101] For example, if a user records in a smartphone app that "this morning's breakfast was a vegetable salad and an omelet," and then uses a wearable device to measure that "I was sedentary all day," this data is preprocessed on the device and sent to a server. The server then integrates this data and uses an AI model to evaluate the user's health status. Based on the analysis results, a specific health management plan is generated, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to increase exercise." This plan is notified to the user via the smartphone app, and subsequent plans are further personalized based on feedback from the user on the results of their actual implementation of the plan.

[0102] Prompt Sentence Examples

[0103] Below is an example of a prompt sentence to input to the generative AI model.

[0104] "Today's diet was mostly vegetables, but I didn't exercise much. Please provide me with the optimal health plan based on this data."

[0105] "Recommend a health management plan for next week based on your stress levels and exercise habits over the past week."

[0106] In this way, this system efficiently collects and analyzes personal health data and provides users with optimal health plans, thereby supporting effective health management.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1: Collect data

[0109] Users input their health data using a smartphone app or wearable device. The data input includes dietary details, exercise volume, heart rate, stress level, etc. For example, a user might input "I had a vegetable salad and an omelet for breakfast," and the wearable device would measure "I was sitting all day." This data is sent to the terminal.

[0110] Input: User's health data (text data, image data, audio data, video data)

[0111] Output: Data sent to the terminal

[0112] Step 2: Preprocessing the data

[0113] The device preprocesses the received health data. Specifically, it uses image processing software (e.g., OpenCV) to adjust the resolution of the image data and remove noise. It also converts the voice data into text data using a voice recognition system (e.g., Google Speech-to-Text API). It then uses regular expressions to remove unnecessary characters and spaces from the text data.

[0114] Input: Raw data collected

[0115] Output: Preprocessed data

[0116] Step 3: Integrate the data

[0117] The server receives the preprocessed data and stores it in a database (e.g., MySQL). The server links different types of data (text data, image data, audio data) by user ID and manages them as a unified data set. Data mapping is used in this process.

[0118] Input: Preprocessed data

[0119] Output: A consolidated dataset

[0120] Step 4: Analyze the data

[0121] The server analyzes the integrated data using machine learning algorithms (e.g., TensorFlow or PyTorch). Specifically, it compares past data with current data to evaluate the user's health status. For example, it analyzes past health logs to evaluate lack of exercise and stress levels. In this process, it utilizes a generative AI model to obtain analysis results in real time.

[0122] Input: Integrated dataset

[0123] Output: Analysis results (user's health status evaluation)

[0124] Step 5: Generate a Health Management Plan

[0125] The server generates a customized health management plan based on the analysis results of the AI ​​model. For example, for a user who is assessed as "not getting enough exercise" or "not getting enough vitamins," it creates a specific action plan such as "recommended yoga three times a week" or "eat one type of fruit every day."

[0126] Input: Analysis results

[0127] Output: A customized health plan

[0128] Step 6: Plan Notification

[0129] The server notifies the user of the generated health management plan via a smartphone app or wearable device. For example, the server can notify the user of the plan by sending a push notification to the smartphone app. The server can also notify the wearable device via vibration or an alert.

[0130] Enter: Customized Health Care Plan

[0131] Output: Notification to user (smartphone app, wearable device)

[0132] Step 7: Gather feedback

[0133] Users provide feedback to the smartphone app about the results and impressions of the health management plan they have completed. This feedback data is then sent back to the server and used to improve the next health management plan. For example, based on feedback such as "I like yoga, so I want to continue," the app will make more personalized suggestions.

[0134] Input: User feedback

[0135] Output: Improved next health plan

[0136] (Application example 1)

[0137] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0138] While systems for personal health management existed in the past, there was a lack of systems that could monitor workers' health status in real time and provide individualized health management plans in specific work environments such as factory environments. Furthermore, there were also insufficient systems that could effectively collect worker health data, analyze it immediately, and enable managers to review it. While this led to a demand for improved work efficiency and safety, workers' health management was not adequately managed, resulting in risks such as reduced productivity and health damage.

[0139] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0140] In this invention, the server includes a means for centrally managing data on the health conditions of individuals, a means for preprocessing the data on the health conditions, and a means for integrating the preprocessed data, thereby enabling real-time monitoring of the health conditions of workers in a factory environment and transmitting the monitoring results to the server for managerial confirmation.

[0141] "Personal health data" refers to health-related data such as a user's heart rate, number of steps, calories burned, and sleep time.

[0142] "Preprocessing" refers to the process of converting collected data into a form suitable for data analysis, such as adjusting the resolution of the data, converting audio data into text, and cleaning text data.

[0143] "Integration" is the process of bringing together different types of health data into a single unified dataset.

[0144] "Instant analysis" is the process of analyzing collected data in real time to assess the user's health status.

[0145] A "personalized health management action plan" is a plan that includes specific recommended dietary, exercise, and sleep actions based on the user's health status.

[0146] "User" refers to an individual who provides health data and receives a health management action plan based on that data.

[0147] "Monitoring" refers to the act of monitoring a user's health status in real time.

[0148] A "server" is a computer system for centralized management, preprocessing, integration, and analysis of data.

[0149] "Supervisor" refers to a person in a factory environment who is responsible for monitoring the health of workers and determining appropriate responses.

[0150] "Workers" refers to people who work in factories or on work sites.

[0151] "Factory environment" refers to the entire facility and surrounding environment used for manufacturing and production.

[0152] The present invention is a system for real-time monitoring of worker health in a factory environment and providing personalized health management action plans. This system centralizes and instantly analyzes data on individual health conditions, resulting in an efficient work environment.

[0153] User Data Collection

[0154] The users, or workers, collect health data such as heart rate, number of steps, calories burned, and sleep time from their smartphones or wearable devices, and this data is sent to a server in real time via the devices.

[0155] Data Preprocessing

[0156] The server preprocesses the health data sent by the user, adjusting the data resolution, converting voice data to text, cleaning the text data, and other processes to prepare the data for analysis.

[0157] Data integration

[0158] The server then integrates the pre-processed data, bringing together different forms of health data into a single unified dataset for efficient data analysis.

[0159] AI-powered analysis

[0160] The server analyzes the integrated health data in real time, using a generative AI model to instantly assess the user's health status, for example, determining a worker's stress level or lack of exercise based on their daily health log.

[0161] Generate a health management plan

[0162] The server then generates a personalized health management action plan based on the analysis results, including specific dietary, exercise, and sleep recommendations, such as "eat fruit daily to increase your vitamin intake" or "do yoga three times a week to reduce stress."

[0163] User Notification

[0164] The server then notifies the user of the generated health management plan via their smartphone or wearable device. The notification format is tailored to the user's preferences and device characteristics, and can include push notifications for the mobile app or alerts for the wearable device.

[0165] Gathering feedback

[0166] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0167] Specific examples

[0168] For example, if a worker records their diet on a smartphone app and measures their exercise volume with a wearable device, this data is preprocessed on the device and sent to a server. The server then integrates this data and evaluates the user's health using an AI model. Based on the evaluation results, a specific plan is generated, such as "eat fruit daily to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the results of the user's actual implementation of the plan can be input as feedback, allowing future plans to be further personalized.

[0169] Example of a generative AI model prompt:

[0170] "If a worker appears to be in a high-stress state after eight hours of work and his heart rate is higher than normal, suggest appropriate health management actions, such as taking appropriate breaks and exercising to reduce stress."

[0171] The above is a specific embodiment of the present invention, which makes it possible to effectively and efficiently support the health management of workers in a factory environment.

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1: Collecting personal health data

[0174] Users collect health data such as heart rate, steps, calories burned, and sleep time from their smartphones or wearable devices. This data may be entered manually by the user or automatically recorded by the device.

[0175] Input: Health data such as heart rate, steps, calories burned, and sleep time

[0176] Output: Data collected by smartphones and wearable devices

[0177] Step 2: Preprocessing the data

[0178] The device preprocesses the health data sent by the user, including adjusting the resolution of the data, converting voice data to text, and cleaning the text data, so that the data is in a form suitable for analysis on the server.

[0179] Input: Raw data collected

[0180] Output: Preprocessed data

[0181] Step 3: Integrate the data

[0182] The server then integrates the preprocessed data, combining data of different formats (text, audio, images, video, etc.) into a single unified dataset. Specific operations include changing the order in which the data is viewed and standardizing the format.

[0183] Input: Preprocessed data

[0184] Output: A consolidated dataset

[0185] Step 4: AI analysis

[0186] The server analyzes the integrated health data in real time, using a machine learning generative model to assess the user's health status from the data. For example, if the user's heart rate is high, it can determine whether the user is showing signs of dehydration or stress.

[0187] Input: Integrated dataset

[0188] Output: Health status assessment results

[0189] Step 5: Generate a Health Management Plan

[0190] The server then generates a personalized health management action plan based on the analysis results, with specific recommendations such as "eat fruit daily to increase your vitamin intake" or "do yoga three times a week to reduce stress."

[0191] Input: Health status assessment results

[0192] Output: personalized health care plan

[0193] Step 6: Notify users

[0194] The server then sends the generated health management plan to the user's smartphone or wearable device, which then sends a push notification or alert.

[0195] Enter: personalized health care plans.

[0196] Output: Plan notified to user's device

[0197] Step 7: Gather feedback

[0198] The user inputs the results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and is used to generate future plans.

[0199] Input: User feedback

[0200] Output: Data used to generate improved plans

[0201] The above are the specific processing steps of the system program of the present invention.

[0202] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0203] The present invention combines a system that centrally collects, pre-processes, integrates, and analyzes personal health data in real time to provide a customized health management plan, with an emotion engine that recognizes the user's emotions. Specific embodiments of the present invention will be described in detail below.

[0204] 1. Collection of User Data

[0205] Users use smartphones or wearable devices to input or collect daily health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of their daily meals into a smartphone app and measure their steps and heart rate with a smartwatch.

[0206] 2. Data Preprocessing

[0207] The device preprocesses the health data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes their contents to extract important indicators such as sleep duration and exercise volume.

[0208] 3. Data integration

[0209] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0210] 4. AI-powered analysis

[0211] The server analyzes the integrated health data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it may determine that the user is lacking exercise based on data from the past week.

[0212] 5. Generate a health management plan

[0213] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0214] 6. Emotion Recognition by Emotion Engine

[0215] The server uses the voice and image data provided by the user to recognize the user's emotions using an emotion engine. Specifically, it analyzes voice tone and facial expressions to estimate emotional states such as stress, happiness, and fatigue. For example, it can analyze the user's voice tone and choice of words from their voice memo to estimate that their stress level is high.

[0216] 7. Adjust your plan based on your emotional state

[0217] The server adjusts the generated health management plan based on the user's emotional state as recognized by the emotion engine: for example, if the user is feeling stressed, relaxation and stress relief techniques are added to the plan.

[0218] 8. Notice to Users

[0219] The server notifies the user of the generated and adjusted health management plan via their smartphone or wearable device. The notification format is adjusted according to the user's preferences and the characteristics of the device. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes and have a relaxing massage in the evening."

[0220] 9. Collecting Feedback

[0221] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0222] Specific examples

[0223] For example, if a user records their diet on a smartphone app and measures their exercise volume with a wearable device, these data are preprocessed on the device and sent to a server. Furthermore, if the user adds a voice memo to the app, the emotion engine analyzes the voice and recognizes that the user is feeling stressed. The server integrates these data, evaluates the user's health condition using an AI model, and, taking into account the results of the emotion engine, generates a specific health management plan, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, which will further personalize future plans.

[0224] The above is a specific embodiment of the present invention. The present invention effectively and efficiently supports the user's health management and also makes it possible to provide an optimal plan according to the user's emotional state.

[0225] The processing flow will be explained below.

[0226] Step 1:

[0227] Users input or collect daily health data using smartphones or wearable devices. This data includes dietary information entered into a smartphone app, steps taken and heart rate measured by a smartwatch, and voice memos recording impressions and physical condition.

[0228] Step 2:

[0229] The device preprocesses various data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also cleans the text data and removes unnecessary information. For example, it transcribes voice memos and extracts health-related keywords from their content.

[0230] Step 3:

[0231] The server centrally integrates the preprocessed data. Data collected from different devices is aggregated for each user and managed as a single dataset. For example, food logs from a smartphone, step count data from a smartwatch, and gratitude keywords extracted from voice memos can all be aggregated.

[0232] Step 4:

[0233] The server analyzes the integrated health data, using generative models and machine learning algorithms to assess the user's health status, for example, by analyzing the amount of exercise, diet, and sleep patterns over the past week to determine whether the user is physically inactive.

[0234] Step 5:

[0235] The server uses an emotion engine to recognize the user's emotional state based on the user's voice data and image data. For example, it analyzes the tone of the voice memo and facial expressions in the image to determine whether the user is feeling stressed.

[0236] Step 6:

[0237] The server generates a customized health management plan based on the analysis results and the user's emotional state. The plan includes recommended actions to improve the user's health, such as diet, exercise, and sleep. For example, a user who is feeling stressed due to lack of exercise might be advised to "walk 30 minutes every day and do yoga three times a week."

[0238] Step 7:

[0239] The server notifies the user of the generated health management plan. The user can check the plan through push notifications on the smartphone app, emails, alerts on wearable devices, etc. For example, a notification on the smartphone may read, "Today's health management plan: 30 minutes of walking and a relaxing massage in the evening."

[0240] Step 8:

[0241] The user carries out the health management plan they receive. They then input their results and impressions as feedback into the smartphone app. For example, they might record things like, "I walked for 30 minutes today and also had a relaxing massage," or "I felt relaxed after the massage."

[0242] Step 9:

[0243] The server collects user feedback and stores it in a database. Based on this feedback, the next health plan can be further personalized, and the user's health status can be continuously monitored and improved. For example, based on the feedback, the next plan may adjust the walking time or suggest different relaxation methods.

[0244] The above processing steps realize a system that comprehensively manages a user's health data and provides an optimal health management plan that takes into account the user's emotional state.

[0245] Example 2

[0246] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0247] Modern health management systems are capable of collecting and centrally managing personal health data. However, these systems often lack the ability to analyze data in real time and generate personalized health management plans. Furthermore, they lack the ability to provide health management plans that take into account the user's emotional state. Therefore, there is a need for a system that can collect, preprocess, integrate, and analyze personal health and emotional data in real time and provide customized health management plans that take into account the user's emotional state.

[0248] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0249] In this invention, the server includes means for centrally collecting personal health data, means for preprocessing the health data, means for integrating the preprocessed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for notifying the user of the health management plan, means for collecting and analyzing emotional data of the user, and means for adjusting the health management plan based on the emotional data. This makes it possible to monitor the health status of an individual in detail in real time and provide an optimal health management plan according to the emotional state.

[0250] "Means for collection" refers to the method or device that allows users to input daily health data using a smartphone or wearable device and have it imported into the system.

[0251] "Preprocessing means" refers to methods or devices that organize and transform collected health data into a format suitable for analysis, such as standardizing the resolution of image data or converting audio data into text.

[0252] The "integrating means" refers to a method or device for consolidating the preprocessed data, associating it with each user, and storing it in a database.

[0253] "Real-time analysis means" means a method or device for analyzing the integrated data in real time to assess the user's health status, including using generative models or machine learning algorithms.

[0254] A "means for generating a healthcare plan" is a method or device for creating an individualized healthcare plan based on the results of real-time analysis.

[0255] The "means for notifying the user" refers to a method or device for transmitting the generated health management plan to the user's smartphone or wearable device to notify the user.

[0256] "Means for collecting and analyzing emotional data" refers to a method or device for recognizing and analyzing the emotional state of a user using voice data or image data provided by the user.

[0257] "Means for adjusting a healthcare plan based on emotional data" refers to a method or apparatus for further optimizing or modifying an already generated healthcare plan based on a recognized emotional state.

[0258] This invention is a system that centrally collects, preprocesses, integrates, and analyzes personal health and emotional data in real time to generate and provide a customized health management plan. This system also takes into account the user's emotional state, providing more personalized health management.

[0259] User Data Collection

[0260] Users use smartphones or wearable devices to input or collect daily health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of their daily meals into a smartphone app and measure their steps and heart rate with a smartwatch.

[0261] Data Preprocessing

[0262] The device preprocesses the health data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes their contents to extract important indicators such as sleep duration and exercise volume.

[0263] Data integration

[0264] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0265] AI-powered analysis

[0266] The server analyzes the integrated health data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it determines that the user is lacking exercise based on data from the past week.

[0267] Generate a health management plan

[0268] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0269] Emotion recognition by emotion engine

[0270] The server uses the voice and image data provided by the user to recognize the user's emotions using an emotion engine. Specifically, it analyzes voice tone and facial expressions to estimate emotional states such as stress, happiness, and fatigue. For example, it can analyze the user's voice tone and choice of words from their voice memo to estimate that their stress level is high.

[0271] Adjusting your plan based on your emotional state

[0272] The server adjusts the generated health management plan based on the user's emotional state as recognized by the emotion engine: for example, if the user is feeling stressed, relaxation and stress relief techniques are added to the plan.

[0273] User Notification

[0274] The server then sends the generated and adjusted health management plan to the user's smartphone or wearable device. The notification format is adjusted according to the user's preferences and the characteristics of the device. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes and have a relaxing massage in the evening."

[0275] Gathering feedback

[0276] Users input their results and impressions of the health management plan they have implemented as feedback. The server receives this feedback and uses it to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0277] Specific examples

[0278] For example, if a user records their diet in a smartphone app and measures their exercise volume with a wearable device, these data are preprocessed on the device and sent to a server. Furthermore, if the user adds a voice memo to the app, the emotion engine analyzes the voice and recognizes that the user is feeling stressed. The server integrates these data, evaluates the user's health status using a generative model, and, taking into account the results of the emotion engine, generates a specific health management plan, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, which will further personalize future plans.

[0279] Example prompts to be input to the generative AI model

[0280] "Could you please give me some advice on diet management? My current diet consists of oatmeal for breakfast, salad for lunch, and pasta for dinner."

[0281] "I feel like I haven't been getting enough exercise lately. Please tell me a specific exercise plan."

[0282] "My stress levels are high. How can I relax?"

[0283] "Generate a health plan for this week. I've already entered my daily steps, meals, and sleep."

[0284] The above is a specific embodiment of the present invention. The present invention effectively and efficiently supports the user's health management and also makes it possible to provide an optimal plan according to the user's emotional state.

[0285] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0286] Step 1:

[0287] Users collect health data using smartphones and wearable devices.

[0288] Specific operation: The user enters the details of their meal into a smartphone app, and the wearable device measures their exercise volume and heart rate.

[0289] Input: Text data and sensor data on diet, exercise, and sleep.

[0290] Output: Collected health data.

[0291] Step 2:

[0292] The device preprocesses the collected health data.

[0293] Specific operations: Image data is resized to 500x500 pixels, voice memos are converted to text using voice recognition software, and the text data is analyzed to extract key information.

[0294] Input: Collected health data (text, images, audio, etc.).

[0295] Output: Preprocessed health data (standardized images, transcribed audio, parsed text).

[0296] Step 3:

[0297] The server consolidates the pre-processed data.

[0298] Specific operation: Meal data from the smartphone and exercise data from the wearable device are associated with the user ID and stored in a centralized database.

[0299] Input: Preprocessed health data.

[0300] Output: Integrated health database.

[0301] Step 4:

[0302] The server analyzes the integrated data in real time using generative AI models.

[0303] What it does: Input the integrated data into an analytical model to calculate stress levels and physical inactivity scores.

[0304] Input: Integrated Health Database.

[0305] Output: User's health assessment (stress level, physical inactivity score, etc.).

[0306] Step 5:

[0307] The server generates a customized health care plan based on the analysis results.

[0308] Specific actions: Based on the analysis results, specific instructions such as "walk 30 minutes every day" and "eat foods rich in vitamin D" are created.

[0309] Input: User's health assessment.

[0310] Output: A customized health care plan.

[0311] Step 6:

[0312] The server collects and analyzes the emotion data.

[0313] Specific operation: Using voice and image data provided by the user, the system analyzes voice tone and facial expressions to recognize the user's emotional state.

[0314] Input: Audio data, image data.

[0315] Output: The user's emotional state (stress, happiness, fatigue, etc.).

[0316] Step 7:

[0317] The server adjusts the health management plan based on the emotion data.

[0318] Specific behavior: If the user's stress level is high, add relaxation and stress relief actions to the health management plan.

[0319] Input: User's emotional state, customized health care plan.

[0320] Output: An adjusted health care plan that takes into account your emotional state.

[0321] Step 8:

[0322] The server notifies the user of the generated and adjusted health care plan.

[0323] Specific operation: A push notification is sent to the smartphone, informing the user of the health management plan for today: 30 minutes of walking and a relaxing massage in the evening.

[0324] Enter: Coordinated Health Care Plan.

[0325] Output: A notification message to the user.

[0326] Step 9:

[0327] The user inputs the results and impressions of the health management plan they have implemented as feedback. The server receives this feedback and reflects it in the generation of subsequent plans.

[0328] Specific operation: The user writes in the smartphone app, "I walked for 30 minutes today" and "I felt good," and the server reflects this information in the next plan.

[0329] Input: User feedback.

[0330] Output: Feedback data reflected in future plans.

[0331] (Application example 2)

[0332] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0333] In recent years, personal health management has become increasingly important, with a particular need for real-time monitoring of health status and appropriate feedback. However, conventional systems have limitations in collecting and analyzing health data and providing personalized health plans, making it difficult to simultaneously manage workers' health and emotional states, particularly in the workplace. Furthermore, health management plans are not appropriately adjusted according to work conditions and emotions, making it difficult to achieve sufficient results in improving work efficiency and maintaining health.

[0334] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for centrally collecting personal health data, means for pre-processing the health data, means for integrating the pre-processed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for collecting emotional data, means for analyzing the emotional data, means for adjusting the health management plan based on the analyzed emotional data, and means for notifying the user of the health management plan. This makes it possible to simultaneously manage the health state and emotional state of a worker and propose optimal work plans or break times in real time.

[0335] "Personal health data" refers to information such as heart rate, steps taken, and exercise volume collected through devices such as smartwatches and smartphones.

[0336] "Preprocessing" refers to the process of extracting necessary information from collected raw data using techniques such as syntax analysis and standardization, thereby improving the quality of the data.

[0337] "Integration" refers to aggregating preprocessed data into a centralized database such as a cloud server and managing it as a unified data set.

[0338] "Real-time analysis" refers to the immediate assessment of the current state based on collected and integrated data, including analysis using generative models and machine learning algorithms.

[0339] A "customized health management plan" refers to a plan that provides specific advice on optimal diet, exercise, rest, etc. based on an individual's health and emotional data.

[0340] "Emotional data" refers to information about a user's emotional state analyzed by the emotion engine using voice, facial expressions, etc.

[0341] An "emotion engine" is a system that analyzes voice tone and facial expressions to estimate a user's emotional state, such as stress, happiness, or fatigue.

[0342] "Adjusting a health management plan" refers to optimizing and modifying an existing health management plan based on the analyzed emotional data.

[0343] "Notification" refers to the act of informing the user of a generated or adjusted health management plan by sending a notification to a smartphone or wearable device.

[0344] "Suggesting work plans or break times" means providing workers with optimal work progress methods and appropriate break times based on health and emotional data.

[0345] The present invention relates to a system that manages and analyzes the health and emotional state of factory workers in real time and provides optimal work plans and break times.

[0346] Health data collection

[0347] Users use smartwatches or smartphones to collect and input daily health data such as heart rate, steps taken, and exercise volume, including data automatically collected through wearable devices.

[0348] Collecting Emotional Data

[0349] The device (e.g., a robot in a factory) collects the voice and facial expressions of the worker using CCTV cameras and audio microphones, and then uses an emotion engine to recognize the user's emotional state and estimate their state of stress, happiness, fatigue, etc. in real time.

[0350] Data Preprocessing

[0351] The collected health and emotion data is preprocessed by the device's on-board computer, which converts voice data into text, standardizes the resolution of image data, and removes unnecessary information through syntactic analysis to extract key indicators.

[0352] Data integration

[0353] The pre-processed data is sent to a cloud server and integrated into a centralized database. By centrally managing health and emotion data for each user, subsequent analysis processes can be carried out efficiently.

[0354] AI-powered analysis

[0355] The server analyzes the integrated data in real time using AI models (e.g., generative models such as TensorFlow or PyTorch) to assess the health and emotional state of workers and predict key health indicators such as stress levels and lack of exercise.

[0356] Generate a health management plan

[0357] The server generates a customized health management plan based on the analysis results. This plan takes into account the worker's health and emotional state and provides specific recommendations for optimal work schedules and break times. For example, it may include specific instructions such as "Take 10 minutes of relaxation time" or "Take a 5-minute break for deep breathing."

[0358] User Notification

[0359] The generated health management plan is sent from the server to the user via their device, such as a smartphone or wearable device, in real time.

[0360] Gathering feedback

[0361] Users input their results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and used to generate future plans. The system continuously monitors the user's health and emotional state and supports improvement.

[0362] Specific examples

[0363] For example, heart rate and step count data collected by the user's smartwatch is sent to a cloud server, while the robot's CCTV camera analyzes the worker's facial expressions in real time to assess their stress level. As a result, the server generates a health management plan such as "Take 10 minutes to relax," and notifies the worker via their smartphone.

[0364] Prompt Sentence Examples

[0365] "Recommend optimal break plans based on workers' health and emotional data. Health data includes heart rate and number of steps. Emotion data includes emotion labels based on facial expressions."

[0366] The present invention makes it possible to simultaneously manage the health and emotional state of factory workers and provide appropriate feedback in real time, thereby effectively supporting work efficiency and worker health.

[0367] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0368] Step 1:

[0369] Users collect and input health data using a smartwatch or smartphone. This data includes heart rate, number of steps, and amount of exercise. The collected data is sent from the wearable device to the smartphone. Input: Heart rate, number of steps, amount of exercise. Output: Health data transferred to the device.

[0370] Step 2:

[0371] The terminal collects the voices and facial expressions of workers using CCTV cameras and audio microphones. This includes collecting voice data and image data in real time. Input: Voice data, facial expression data. Output: Emotion data collected in real time.

[0372] Step 3:

[0373] The device preprocesses the collected health and emotion data. Voice data is converted to text (for example, using voice recognition software), and image data has its resolution standardized. It also removes unnecessary information from the collected data and extracts important information. Input: Voice data, image data. Output: Preprocessed health and emotion data.

[0374] Step 4:

[0375] The preprocessed data is sent from the device to a cloud server and integrated into a centralized database. This allows the data to be organized by user. Input: Preprocessed health data and emotion data. Output: Integrated data on the cloud server.

[0376] Step 5:

[0377] The server analyzes the integrated data in real time using an AI model (e.g., a generative model such as TensorFlow or PyTorch). Health and emotional states are evaluated, and important indicators such as stress levels and lack of exercise are predicted. Input: Integrated data. Output: Analysis results (evaluation of health and emotional states).

[0378] Step 6:

[0379] The server generates a customized health management plan based on the analysis results. This plan includes specific work plans and rest times and is created for real-time feedback. Input: Analysis results. Output: Health management plan.

[0380] Step 7:

[0381] The server notifies the user of the generated health management plan via their smartphone or wearable device. This notification is done in real time, providing the user with optimal advice. Input: Health management plan. Output: Plan notified to the user.

[0382] Step 8:

[0383] The user inputs the results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and used to generate future plans. Input: Feedback data. Output: Feedback data sent to the server.

[0384] Step 9:

[0385] The server updates the database and AI model based on the collected feedback, continuously improving the system, which allows it to provide more accurate and personalized plans for the user's health and emotional state. Input: Feedback data. Output: Updated database and AI model.

[0386] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0387] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0388] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0389] [Second embodiment]

[0390] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0391] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0392] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0393] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0394] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0395] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0396] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0397] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0398] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0399] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0400] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0401] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0402] The present invention provides a system for centrally collecting, pre-processing, integrating, and analyzing personal health data in real time to provide a customized health management plan. Specific embodiments of the system are described in detail below.

[0403] 1. Collection of User Data

[0404] Users use smartphones or wearable devices to input their daily health data, which includes text data (health diaries and survey responses), audio data (voice memos), image data (scanned health records), and video data (exercise videos, etc.).

[0405] 2. Data Preprocessing

[0406] The device preprocesses the health data sent by the user, specifically adjusting the resolution of image data, converting audio data to text, and cleaning and converting text data into a unified format. This preprocessing makes the data suitable for subsequent integration and analysis.

[0407] 3. Data integration

[0408] The server then integrates the pre-processed data, combining data from different formats into a single unified dataset, allowing individual users' health data to be managed centrally.

[0409] 4. AI-powered analysis

[0410] The server analyzes the integrated health data, using a generative model (e.g., a machine learning algorithm) to assess the user's health status from a variety of data. For example, it determines stress levels and lack of exercise from daily health logs.

[0411] 5. Generate a health management plan

[0412] The server generates a customized health management plan based on the analysis results, which includes specific dietary, exercise, and sleep recommendations, allowing users to receive advice optimized for their health status.

[0413] 6. Notice to Users

[0414] The server then notifies the user of the generated health management plan via their smartphone or wearable device. The notification format is tailored to the user's preferences and the characteristics of the device, and may include push notifications for the mobile app or alerts for the wearable device.

[0415] 7. Gathering Feedback

[0416] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0417] Specific examples

[0418] For example, if a user records their diet on a smartphone app and measures their exercise volume with a wearable device, this data is preprocessed on the device and sent to a server. The server then integrates this data and evaluates the user's health using an AI model. Based on the evaluation results, a specific plan is generated, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, allowing future plans to be further personalized.

[0419] The above is a specific embodiment of the present invention, which makes it possible to effectively and efficiently support the health management of a user.

[0420] The processing flow will be explained below.

[0421] Step 1:

[0422] Users use smartphones or wearable devices to input or collect health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of the day's meals into a smartphone app and measure the number of steps taken and heart rate with a smartwatch.

[0423] Step 2:

[0424] The device receives the collected data and performs preprocessing. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes the content to extract important indicators such as sleep time and exercise volume.

[0425] Step 3:

[0426] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0427] Step 4:

[0428] The server analyzes the integrated data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it may determine that the user is lacking exercise based on data from the past week.

[0429] Step 5:

[0430] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0431] Step 6:

[0432] The server notifies the user of the generated health management plan. The plan is communicated to the user using push notifications on the smartphone app, emails, and alerts on wearable devices. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes."

[0433] Step 7:

[0434] The user carries out the health management plan they receive. They then input their results and impressions into the smartphone app as feedback. For example, they can record in the app that they "walked for 30 minutes today" and enter comments about their physical condition and impressions during the walk.

[0435] Step 8:

[0436] The server collects feedback from users and stores it in a database. This feedback data is used to improve the accuracy of the next health management plan. For example, if a user gives feedback that "30 minutes of walking is too strenuous," the server will suggest a 15-minute walk next time.

[0437] The above processing steps realize a system that efficiently manages a user's health data and provides an optimal health management plan according to the individual health condition.

[0438] Example 1

[0439] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0440] In modern society, personal health management is extremely important, and there is a demand for providing appropriate health plans based on various data. However, existing health management systems have difficulty efficiently collecting, integrating, and analyzing data from multiple data sources, making it difficult to provide customized plans tailored to users in real time. Furthermore, the lack of a function to improve plans based on user feedback has limited the effectiveness of long-term health management.

[0441] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0442] In this invention, the server includes means for centrally collecting personal health data, means for preprocessing the health data, means for integrating the preprocessed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for notifying the user of the health management plan, and means for collecting feedback from the user and reflecting the feedback in the next plan, thereby making it possible to provide an optimal health management plan for each individual user and to continuously improve it.

[0443] "Personal health data" refers to data about a user's daily life, such as their heart rate, amount of exercise, diet, and stress level.

[0444] "Preprocessing" refers to the process of converting raw data collected from users into a format that is easier to analyze, and specifically includes adjusting resolution, converting speech to text, and cleaning the data.

[0445] "Integration" refers to the process of combining datasets of different formats into one unified dataset.

[0446] "Real-time analysis" refers to the process of instantly analyzing collected data and reflecting the results immediately.

[0447] "Customized Health Plan" refers to a health plan that includes specific recommended dietary, exercise, sleep, and other actions based on a user's individual data.

[0448] "Feedback" refers to information that the user re-enters into the system regarding the results and impressions of the health management plan that he or she has implemented.

[0449] A "machine learning algorithm" refers to a set of computational techniques that allow a computer to analyze data, recognize patterns, and make predictions.

[0450] The present invention is a system that centrally collects, pre-processes, integrates, and analyzes personal health data in real time to provide customized health management plans.

[0451] 1. Collection of User Data

[0452] Users input health data using smartphones or wearable devices. This data includes text data, voice data, image data, and video data. For example, a user might record "Today's breakfast is vegetable salad and an omelet" in a smartphone app, and the wearable device measures the amount of exercise. This data is sent to the terminal in real time.

[0453] 2. Data Preprocessing

[0454] The device preprocesses the received health data. For example, it uses image processing software (e.g., OpenCV) to adjust the resolution of the photos and remove noise. It also converts the audio data into text using a speech recognition system (e.g., Google Speech-to-Text API), and uses regular expressions to remove unnecessary characters and spaces. This converts the data into a format suitable for analysis.

[0455] 3. Data integration

[0456] The server receives the preprocessed data and stores it in a database system (e.g., MySQL). The server then combines data sets of different formats into a unified data set. This process uses data mapping, for example, to link text data, image data, and audio data by user ID.

[0457] 4. AI-powered analysis

[0458] The server analyzes the integrated data using machine learning algorithms (e.g., TensorFlow or PyTorch). The server compares the user's past data with their current data to recognize patterns. For example, it analyzes past health logs to assess lack of exercise and stress levels. In this process, it utilizes generative AI models to assess the user's health status in real time.

[0459] 5. Generate a health management plan

[0460] The server generates a customized health management plan based on the analysis results. For example, based on the analysis results of "lack of exercise" and "insufficient vitamin intake," the server creates a specific action plan such as "recommended yoga three times a week" or "eat fruit every day." It also advises the user on specific meal menus and recommended exercise amounts.

[0461] 6. Notice to Users

[0462] The server notifies the user of the generated health management plan via a smartphone app or wearable device. For example, it can send a push notification to the smartphone app to inform the user of the new health plan. It can also notify the wearable device via vibration or an alert.

[0463] 7. Gathering Feedback

[0464] Users input the results and impressions of the health management plan they have completed as feedback into the smartphone app. This feedback data is sent back to the server, which analyzes it and uses it to improve the next health management plan. For example, more personalized suggestions can be made based on feedback such as "I like yoga, so I want to continue."

[0465] Specific examples

[0466] For example, if a user records in a smartphone app that "this morning's breakfast was a vegetable salad and an omelet," and then uses a wearable device to measure that "I was sedentary all day," this data is preprocessed on the device and sent to a server. The server then integrates this data and uses an AI model to evaluate the user's health status. Based on the analysis results, a specific health management plan is generated, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to increase exercise." This plan is notified to the user via the smartphone app, and subsequent plans are further personalized based on feedback from the user on the results of their actual implementation of the plan.

[0467] Prompt Sentence Examples

[0468] Below is an example of a prompt sentence to input to the generative AI model.

[0469] "Today's diet was mostly vegetables, but I didn't exercise much. Please provide me with the optimal health plan based on this data."

[0470] "Recommend a health management plan for next week based on your stress levels and exercise habits over the past week."

[0471] In this way, this system efficiently collects and analyzes personal health data and provides users with optimal health plans, thereby supporting effective health management.

[0472] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0473] Step 1: Collect data

[0474] Users input their health data using a smartphone app or wearable device. The data input includes dietary details, exercise volume, heart rate, stress level, etc. For example, a user might input "I had a vegetable salad and an omelet for breakfast," and the wearable device would measure "I was sitting all day." This data is sent to the terminal.

[0475] Input: User's health data (text data, image data, audio data, video data)

[0476] Output: Data sent to the terminal

[0477] Step 2: Preprocessing the data

[0478] The device preprocesses the received health data. Specifically, it uses image processing software (e.g., OpenCV) to adjust the resolution of the image data and remove noise. It also converts the voice data into text data using a voice recognition system (e.g., Google Speech-to-Text API). It then uses regular expressions to remove unnecessary characters and spaces from the text data.

[0479] Input: Raw data collected

[0480] Output: Preprocessed data

[0481] Step 3: Integrate the data

[0482] The server receives the preprocessed data and stores it in a database (e.g., MySQL). The server links different types of data (text data, image data, audio data) by user ID and manages them as a unified data set. Data mapping is used in this process.

[0483] Input: Preprocessed data

[0484] Output: A consolidated dataset

[0485] Step 4: Analyze the data

[0486] The server analyzes the integrated data using machine learning algorithms (e.g., TensorFlow or PyTorch). Specifically, it compares past data with current data to evaluate the user's health status. For example, it analyzes past health logs to evaluate lack of exercise and stress levels. In this process, it utilizes a generative AI model to obtain analysis results in real time.

[0487] Input: Integrated dataset

[0488] Output: Analysis results (user's health status evaluation)

[0489] Step 5: Generate a Health Management Plan

[0490] The server generates a customized health management plan based on the analysis results of the AI ​​model. For example, for a user who is assessed as "not getting enough exercise" or "not getting enough vitamins," it creates a specific action plan such as "recommended yoga three times a week" or "eat one type of fruit every day."

[0491] Input: Analysis results

[0492] Output: A customized health plan

[0493] Step 6: Plan Notification

[0494] The server notifies the user of the generated health management plan via a smartphone app or wearable device. For example, the server can notify the user of the plan by sending a push notification to the smartphone app. The server can also notify the wearable device via vibration or an alert.

[0495] Enter: Customized Health Care Plan

[0496] Output: Notification to user (smartphone app, wearable device)

[0497] Step 7: Gather feedback

[0498] Users provide feedback to the smartphone app about the results and impressions of the health management plan they have completed. This feedback data is then sent back to the server and used to improve the next health management plan. For example, based on feedback such as "I like yoga, so I want to continue," the app will make more personalized suggestions.

[0499] Input: User feedback

[0500] Output: Improved next health plan

[0501] (Application example 1)

[0502] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0503] While systems for personal health management existed in the past, there was a lack of systems that could monitor workers' health status in real time and provide individualized health management plans in specific work environments such as factory environments. Furthermore, there were also insufficient systems that could effectively collect worker health data, analyze it immediately, and enable managers to review it. While this led to a demand for improved work efficiency and safety, workers' health management was not adequately managed, resulting in risks such as reduced productivity and health damage.

[0504] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0505] In this invention, the server includes a means for centrally managing data on the health conditions of individuals, a means for preprocessing the data on the health conditions, and a means for integrating the preprocessed data, thereby enabling real-time monitoring of the health conditions of workers in a factory environment and transmitting the monitoring results to the server for managerial confirmation.

[0506] "Personal health data" refers to health-related data such as a user's heart rate, number of steps, calories burned, and sleep time.

[0507] "Preprocessing" refers to the process of converting collected data into a form suitable for data analysis, such as adjusting the resolution of the data, converting audio data into text, and cleaning text data.

[0508] "Integration" is the process of bringing together different types of health data into a single unified dataset.

[0509] "Instant analysis" is the process of analyzing collected data in real time to assess the user's health status.

[0510] A "personalized health management action plan" is a plan that includes specific recommended dietary, exercise, and sleep actions based on the user's health status.

[0511] "User" refers to an individual who provides health data and receives a health management action plan based on that data.

[0512] "Monitoring" refers to the act of monitoring a user's health status in real time.

[0513] A "server" is a computer system for centralized management, preprocessing, integration, and analysis of data.

[0514] "Supervisor" refers to a person in a factory environment who is responsible for monitoring the health of workers and determining appropriate responses.

[0515] "Workers" refers to people who work in factories or on work sites.

[0516] "Factory environment" refers to the entire facility and surrounding environment used for manufacturing and production.

[0517] The present invention is a system for real-time monitoring of worker health in a factory environment and providing personalized health management action plans. This system centralizes and instantly analyzes data on individual health conditions, resulting in an efficient work environment.

[0518] User Data Collection

[0519] The users, or workers, collect health data such as heart rate, number of steps, calories burned, and sleep time from their smartphones or wearable devices, and this data is sent to a server in real time via the devices.

[0520] Data Preprocessing

[0521] The server preprocesses the health data sent by the user, adjusting the data resolution, converting voice data to text, cleaning the text data, and other processes to prepare the data for analysis.

[0522] Data integration

[0523] The server then integrates the pre-processed data, bringing together different forms of health data into a single unified dataset for efficient data analysis.

[0524] AI-powered analysis

[0525] The server analyzes the integrated health data in real time, using a generative AI model to instantly assess the user's health status, for example, determining a worker's stress level or lack of exercise based on their daily health log.

[0526] Generate a health management plan

[0527] The server then generates a personalized health management action plan based on the analysis results, including specific dietary, exercise, and sleep recommendations, such as "eat fruit daily to increase your vitamin intake" or "do yoga three times a week to reduce stress."

[0528] User Notification

[0529] The server then notifies the user of the generated health management plan via their smartphone or wearable device. The notification format is tailored to the user's preferences and device characteristics, and can include push notifications for the mobile app or alerts for the wearable device.

[0530] Gathering feedback

[0531] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0532] Specific examples

[0533] For example, if a worker records their diet on a smartphone app and measures their exercise volume with a wearable device, this data is preprocessed on the device and sent to a server. The server then integrates this data and evaluates the user's health using an AI model. Based on the evaluation results, a specific plan is generated, such as "eat fruit daily to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the results of the user's actual implementation of the plan can be input as feedback, allowing future plans to be further personalized.

[0534] Example of a generative AI model prompt:

[0535] "If a worker appears to be in a high-stress state after eight hours of work and his heart rate is higher than normal, suggest appropriate health management actions, such as taking appropriate breaks and exercising to reduce stress."

[0536] The above is a specific embodiment of the present invention, which makes it possible to effectively and efficiently support the health management of workers in a factory environment.

[0537] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0538] Step 1: Collecting personal health data

[0539] Users collect health data such as heart rate, steps, calories burned, and sleep time from their smartphones or wearable devices. This data may be entered manually by the user or automatically recorded by the device.

[0540] Input: Health data such as heart rate, steps, calories burned, and sleep time

[0541] Output: Data collected by smartphones and wearable devices

[0542] Step 2: Preprocessing the data

[0543] The device preprocesses the health data sent by the user, including adjusting the resolution of the data, converting voice data to text, and cleaning the text data, so that the data is in a form suitable for analysis on the server.

[0544] Input: Raw data collected

[0545] Output: Preprocessed data

[0546] Step 3: Integrate the data

[0547] The server then integrates the preprocessed data, combining data of different formats (text, audio, images, video, etc.) into a single unified dataset. Specific operations include changing the order in which the data is viewed and standardizing the format.

[0548] Input: Preprocessed data

[0549] Output: A consolidated dataset

[0550] Step 4: AI analysis

[0551] The server analyzes the integrated health data in real time, using a machine learning generative model to assess the user's health status from the data. For example, if the user's heart rate is high, it can determine whether the user is showing signs of dehydration or stress.

[0552] Input: Integrated dataset

[0553] Output: Health status assessment results

[0554] Step 5: Generate a Health Management Plan

[0555] The server then generates a personalized health management action plan based on the analysis results, with specific recommendations such as "eat fruit daily to increase your vitamin intake" or "do yoga three times a week to reduce stress."

[0556] Input: Health status assessment results

[0557] Output: personalized health care plan

[0558] Step 6: Notify users

[0559] The server then sends the generated health management plan to the user's smartphone or wearable device, which then sends a push notification or alert.

[0560] Enter: personalized health care plans.

[0561] Output: Plan notified to user's device

[0562] Step 7: Gather feedback

[0563] The user inputs the results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and is used to generate future plans.

[0564] Input: User feedback

[0565] Output: Data used to generate improved plans

[0566] The above are the specific processing steps of the system program of the present invention.

[0567] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0568] The present invention combines a system that centrally collects, pre-processes, integrates, and analyzes personal health data in real time to provide a customized health management plan, with an emotion engine that recognizes the user's emotions. Specific embodiments of the present invention will be described in detail below.

[0569] 1. Collection of User Data

[0570] Users use smartphones or wearable devices to input or collect daily health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of their daily meals into a smartphone app and measure their steps and heart rate with a smartwatch.

[0571] 2. Data Preprocessing

[0572] The device preprocesses the health data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes their contents to extract important indicators such as sleep duration and exercise volume.

[0573] 3. Data integration

[0574] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0575] 4. AI-powered analysis

[0576] The server analyzes the integrated health data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it may determine that the user is lacking exercise based on data from the past week.

[0577] 5. Generate a health management plan

[0578] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0579] 6. Emotion Recognition by Emotion Engine

[0580] The server uses the voice and image data provided by the user to recognize the user's emotions using an emotion engine. Specifically, it analyzes voice tone and facial expressions to estimate emotional states such as stress, happiness, and fatigue. For example, it can analyze the user's voice tone and choice of words from their voice memo to estimate that their stress level is high.

[0581] 7. Adjust your plan based on your emotional state

[0582] The server adjusts the generated health management plan based on the user's emotional state as recognized by the emotion engine: for example, if the user is feeling stressed, relaxation and stress relief techniques are added to the plan.

[0583] 8. Notice to Users

[0584] The server notifies the user of the generated and adjusted health management plan via their smartphone or wearable device. The notification format is adjusted according to the user's preferences and the characteristics of the device. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes and have a relaxing massage in the evening."

[0585] 9. Collecting Feedback

[0586] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0587] Specific examples

[0588] For example, if a user records their diet on a smartphone app and measures their exercise volume with a wearable device, these data are preprocessed on the device and sent to a server. Furthermore, if the user adds a voice memo to the app, the emotion engine analyzes the voice and recognizes that the user is feeling stressed. The server integrates these data, evaluates the user's health condition using an AI model, and, taking into account the results of the emotion engine, generates a specific health management plan, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, which will further personalize future plans.

[0589] The above is a specific embodiment of the present invention. The present invention effectively and efficiently supports the user's health management and also makes it possible to provide an optimal plan according to the user's emotional state.

[0590] The processing flow will be explained below.

[0591] Step 1:

[0592] Users input or collect daily health data using smartphones or wearable devices. This data includes dietary information entered into a smartphone app, steps taken and heart rate measured by a smartwatch, and voice memos recording impressions and physical condition.

[0593] Step 2:

[0594] The device preprocesses various data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also cleans the text data and removes unnecessary information. For example, it transcribes voice memos and extracts health-related keywords from their content.

[0595] Step 3:

[0596] The server centrally integrates the preprocessed data. Data collected from different devices is aggregated for each user and managed as a single dataset. For example, food logs from a smartphone, step count data from a smartwatch, and gratitude keywords extracted from voice memos can all be aggregated.

[0597] Step 4:

[0598] The server analyzes the integrated health data, using generative models and machine learning algorithms to assess the user's health status, for example, by analyzing the amount of exercise, diet, and sleep patterns over the past week to determine whether the user is physically inactive.

[0599] Step 5:

[0600] The server uses an emotion engine to recognize the user's emotional state based on the user's voice data and image data. For example, it analyzes the tone of the voice memo and facial expressions in the image to determine whether the user is feeling stressed.

[0601] Step 6:

[0602] The server generates a customized health management plan based on the analysis results and the user's emotional state. The plan includes recommended actions to improve the user's health, such as diet, exercise, and sleep. For example, a user who is feeling stressed due to lack of exercise might be advised to "walk 30 minutes every day and do yoga three times a week."

[0603] Step 7:

[0604] The server notifies the user of the generated health management plan. The user can check the plan through push notifications on the smartphone app, emails, alerts on wearable devices, etc. For example, a notification on the smartphone may read, "Today's health management plan: 30 minutes of walking and a relaxing massage in the evening."

[0605] Step 8:

[0606] The user carries out the health management plan they receive. They then input their results and impressions as feedback into the smartphone app. For example, they might record things like, "I walked for 30 minutes today and also had a relaxing massage," or "I felt relaxed after the massage."

[0607] Step 9:

[0608] The server collects user feedback and stores it in a database. Based on this feedback, the next health plan can be further personalized, and the user's health status can be continuously monitored and improved. For example, based on the feedback, the next plan may adjust the walking time or suggest different relaxation methods.

[0609] The above processing steps realize a system that comprehensively manages a user's health data and provides an optimal health management plan that takes into account the user's emotional state.

[0610] Example 2

[0611] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0612] Modern health management systems are capable of collecting and centrally managing personal health data. However, these systems often lack the ability to analyze data in real time and generate personalized health management plans. Furthermore, they lack the ability to provide health management plans that take into account the user's emotional state. Therefore, there is a need for a system that can collect, preprocess, integrate, and analyze personal health and emotional data in real time and provide customized health management plans that take into account the user's emotional state.

[0613] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0614] In this invention, the server includes means for centrally collecting personal health data, means for preprocessing the health data, means for integrating the preprocessed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for notifying the user of the health management plan, means for collecting and analyzing emotional data of the user, and means for adjusting the health management plan based on the emotional data. This makes it possible to monitor the health status of an individual in detail in real time and provide an optimal health management plan according to the emotional state.

[0615] "Means for collection" refers to the method or device that allows users to input daily health data using a smartphone or wearable device and have it imported into the system.

[0616] "Preprocessing means" refers to methods or devices that organize and transform collected health data into a format suitable for analysis, such as standardizing the resolution of image data or converting audio data into text.

[0617] The "integrating means" refers to a method or device for consolidating the preprocessed data, associating it with each user, and storing it in a database.

[0618] "Real-time analysis means" means a method or device for analyzing the integrated data in real time to assess the user's health status, including using generative models or machine learning algorithms.

[0619] A "means for generating a healthcare plan" is a method or device for creating an individualized healthcare plan based on the results of real-time analysis.

[0620] The "means for notifying the user" refers to a method or device for transmitting the generated health management plan to the user's smartphone or wearable device to notify the user.

[0621] "Means for collecting and analyzing emotional data" refers to a method or device for recognizing and analyzing the emotional state of a user using voice data or image data provided by the user.

[0622] "Means for adjusting a healthcare plan based on emotional data" refers to a method or apparatus for further optimizing or modifying an already generated healthcare plan based on a recognized emotional state.

[0623] This invention is a system that centrally collects, preprocesses, integrates, and analyzes personal health and emotional data in real time to generate and provide a customized health management plan. This system also takes into account the user's emotional state, providing more personalized health management.

[0624] User Data Collection

[0625] Users use smartphones or wearable devices to input or collect daily health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of their daily meals into a smartphone app and measure their steps and heart rate with a smartwatch.

[0626] Data Preprocessing

[0627] The device preprocesses the health data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes their contents to extract important indicators such as sleep duration and exercise volume.

[0628] Data integration

[0629] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0630] AI-powered analysis

[0631] The server analyzes the integrated health data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it determines that the user is lacking exercise based on data from the past week.

[0632] Generate a health management plan

[0633] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0634] Emotion recognition by emotion engine

[0635] The server uses the voice and image data provided by the user to recognize the user's emotions using an emotion engine. Specifically, it analyzes voice tone and facial expressions to estimate emotional states such as stress, happiness, and fatigue. For example, it can analyze the user's voice tone and choice of words from their voice memo to estimate that their stress level is high.

[0636] Adjusting your plan based on your emotional state

[0637] The server adjusts the generated health management plan based on the user's emotional state as recognized by the emotion engine: for example, if the user is feeling stressed, relaxation and stress relief techniques are added to the plan.

[0638] User Notification

[0639] The server then sends the generated and adjusted health management plan to the user's smartphone or wearable device. The notification format is adjusted according to the user's preferences and the characteristics of the device. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes and have a relaxing massage in the evening."

[0640] Gathering feedback

[0641] Users input their results and impressions of the health management plan they have implemented as feedback. The server receives this feedback and uses it to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0642] Specific examples

[0643] For example, if a user records their diet in a smartphone app and measures their exercise volume with a wearable device, these data are preprocessed on the device and sent to a server. Furthermore, if the user adds a voice memo to the app, the emotion engine analyzes the voice and recognizes that the user is feeling stressed. The server integrates these data, evaluates the user's health status using a generative model, and, taking into account the results of the emotion engine, generates a specific health management plan, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, which will further personalize future plans.

[0644] Example prompts to be input to the generative AI model

[0645] "Could you please give me some advice on diet management? My current diet consists of oatmeal for breakfast, salad for lunch, and pasta for dinner."

[0646] "I feel like I haven't been getting enough exercise lately. Please tell me a specific exercise plan."

[0647] "My stress levels are high. How can I relax?"

[0648] "Generate a health plan for this week. I've already entered my daily steps, meals, and sleep."

[0649] The above is a specific embodiment of the present invention. The present invention effectively and efficiently supports the user's health management and also makes it possible to provide an optimal plan according to the user's emotional state.

[0650] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0651] Step 1:

[0652] Users collect health data using smartphones and wearable devices.

[0653] Specific operation: The user enters the details of their meal into a smartphone app, and the wearable device measures their exercise volume and heart rate.

[0654] Input: Text data and sensor data on diet, exercise, and sleep.

[0655] Output: Collected health data.

[0656] Step 2:

[0657] The device preprocesses the collected health data.

[0658] Specific operations: Image data is resized to 500x500 pixels, voice memos are converted to text using voice recognition software, and the text data is analyzed to extract key information.

[0659] Input: Collected health data (text, images, audio, etc.).

[0660] Output: Preprocessed health data (standardized images, transcribed audio, parsed text).

[0661] Step 3:

[0662] The server consolidates the pre-processed data.

[0663] Specific operation: Meal data from the smartphone and exercise data from the wearable device are associated with the user ID and stored in a centralized database.

[0664] Input: Preprocessed health data.

[0665] Output: Integrated health database.

[0666] Step 4:

[0667] The server analyzes the integrated data in real time using generative AI models.

[0668] What it does: Input the integrated data into an analytical model to calculate stress levels and physical inactivity scores.

[0669] Input: Integrated Health Database.

[0670] Output: User's health assessment (stress level, physical inactivity score, etc.).

[0671] Step 5:

[0672] The server generates a customized health care plan based on the analysis results.

[0673] Specific actions: Based on the analysis results, specific instructions such as "walk 30 minutes every day" and "eat foods rich in vitamin D" are created.

[0674] Input: User's health assessment.

[0675] Output: A customized health care plan.

[0676] Step 6:

[0677] The server collects and analyzes the emotion data.

[0678] Specific operation: Using voice and image data provided by the user, the system analyzes voice tone and facial expressions to recognize the user's emotional state.

[0679] Input: Audio data, image data.

[0680] Output: The user's emotional state (stress, happiness, fatigue, etc.).

[0681] Step 7:

[0682] The server adjusts the health management plan based on the emotion data.

[0683] Specific behavior: If the user's stress level is high, add relaxation and stress relief actions to the health management plan.

[0684] Input: User's emotional state, customized health care plan.

[0685] Output: An adjusted health care plan that takes into account your emotional state.

[0686] Step 8:

[0687] The server notifies the user of the generated and adjusted health care plan.

[0688] Specific operation: A push notification is sent to the smartphone, informing the user of the health management plan for today: 30 minutes of walking and a relaxing massage in the evening.

[0689] Enter: Coordinated Health Care Plan.

[0690] Output: A notification message to the user.

[0691] Step 9:

[0692] The user inputs the results and impressions of the health management plan they have implemented as feedback. The server receives this feedback and reflects it in the generation of subsequent plans.

[0693] Specific operation: The user writes in the smartphone app, "I walked for 30 minutes today" and "I felt good," and the server reflects this information in the next plan.

[0694] Input: User feedback.

[0695] Output: Feedback data reflected in future plans.

[0696] (Application example 2)

[0697] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0698] In recent years, personal health management has become increasingly important, with a particular need for real-time monitoring of health status and appropriate feedback. However, conventional systems have limitations in collecting and analyzing health data and providing personalized health plans, making it difficult to simultaneously manage workers' health and emotional states, particularly in the workplace. Furthermore, health management plans are not appropriately adjusted according to work conditions and emotions, making it difficult to achieve sufficient results in improving work efficiency and maintaining health.

[0699] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for centrally collecting personal health data, means for pre-processing the health data, means for integrating the pre-processed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for collecting emotional data, means for analyzing the emotional data, means for adjusting the health management plan based on the analyzed emotional data, and means for notifying the user of the health management plan. This makes it possible to simultaneously manage the health state and emotional state of a worker and propose optimal work plans or break times in real time.

[0700] "Personal health data" refers to information such as heart rate, steps taken, and exercise volume collected through devices such as smartwatches and smartphones.

[0701] "Preprocessing" refers to the process of extracting necessary information from collected raw data using techniques such as syntax analysis and standardization, thereby improving the quality of the data.

[0702] "Integration" refers to aggregating preprocessed data into a centralized database such as a cloud server and managing it as a unified data set.

[0703] "Real-time analysis" refers to the immediate assessment of the current state based on collected and integrated data, including analysis using generative models and machine learning algorithms.

[0704] A "customized health management plan" refers to a plan that provides specific advice on optimal diet, exercise, rest, etc. based on an individual's health and emotional data.

[0705] "Emotional data" refers to information about a user's emotional state analyzed by the emotion engine using voice, facial expressions, etc.

[0706] An "emotion engine" is a system that analyzes voice tone and facial expressions to estimate a user's emotional state, such as stress, happiness, or fatigue.

[0707] "Adjusting a health management plan" refers to optimizing and modifying an existing health management plan based on the analyzed emotional data.

[0708] "Notification" refers to the act of informing the user of a generated or adjusted health management plan by sending a notification to a smartphone or wearable device.

[0709] "Suggesting work plans or break times" means providing workers with optimal work progress methods and appropriate break times based on health and emotional data.

[0710] The present invention relates to a system that manages and analyzes the health and emotional state of factory workers in real time and provides optimal work plans and break times.

[0711] Health data collection

[0712] Users use smartwatches or smartphones to collect and input daily health data such as heart rate, steps taken, and exercise volume, including data automatically collected through wearable devices.

[0713] Collecting Emotional Data

[0714] The device (e.g., a robot in a factory) collects the voice and facial expressions of the worker using CCTV cameras and audio microphones, and then uses an emotion engine to recognize the user's emotional state and estimate their state of stress, happiness, fatigue, etc. in real time.

[0715] Data Preprocessing

[0716] The collected health and emotion data is preprocessed by the device's on-board computer, which converts voice data into text, standardizes the resolution of image data, and removes unnecessary information through syntactic analysis to extract key indicators.

[0717] Data integration

[0718] The pre-processed data is sent to a cloud server and integrated into a centralized database. By centrally managing health and emotion data for each user, subsequent analysis processes can be carried out efficiently.

[0719] AI-powered analysis

[0720] The server analyzes the integrated data in real time using AI models (e.g., generative models such as TensorFlow or PyTorch) to assess the health and emotional state of workers and predict key health indicators such as stress levels and lack of exercise.

[0721] Generate a health management plan

[0722] The server generates a customized health management plan based on the analysis results. This plan takes into account the worker's health and emotional state and provides specific recommendations for optimal work schedules and break times. For example, it may include specific instructions such as "Take 10 minutes of relaxation time" or "Take a 5-minute break for deep breathing."

[0723] User Notification

[0724] The generated health management plan is sent from the server to the user via their device, such as a smartphone or wearable device, in real time.

[0725] Gathering feedback

[0726] Users input their results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and used to generate future plans. The system continuously monitors the user's health and emotional state and supports improvement.

[0727] Specific examples

[0728] For example, heart rate and step count data collected by the user's smartwatch is sent to a cloud server, while the robot's CCTV camera analyzes the worker's facial expressions in real time to assess their stress level. As a result, the server generates a health management plan such as "Take 10 minutes to relax," and notifies the worker via their smartphone.

[0729] Prompt Sentence Examples

[0730] "Recommend optimal break plans based on workers' health and emotional data. Health data includes heart rate and number of steps. Emotion data includes emotion labels based on facial expressions."

[0731] The present invention makes it possible to simultaneously manage the health and emotional state of factory workers and provide appropriate feedback in real time, thereby effectively supporting work efficiency and worker health.

[0732] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0733] Step 1:

[0734] Users collect and input health data using a smartwatch or smartphone. This data includes heart rate, number of steps, and amount of exercise. The collected data is sent from the wearable device to the smartphone. Input: Heart rate, number of steps, amount of exercise. Output: Health data transferred to the device.

[0735] Step 2:

[0736] The terminal collects the voices and facial expressions of workers using CCTV cameras and audio microphones. This includes collecting voice data and image data in real time. Input: Voice data, facial expression data. Output: Emotion data collected in real time.

[0737] Step 3:

[0738] The device preprocesses the collected health and emotion data. Voice data is converted to text (for example, using voice recognition software), and image data has its resolution standardized. It also removes unnecessary information from the collected data and extracts important information. Input: Voice data, image data. Output: Preprocessed health and emotion data.

[0739] Step 4:

[0740] The preprocessed data is sent from the device to a cloud server and integrated into a centralized database. This allows the data to be organized by user. Input: Preprocessed health data and emotion data. Output: Integrated data on the cloud server.

[0741] Step 5:

[0742] The server analyzes the integrated data in real time using an AI model (e.g., a generative model such as TensorFlow or PyTorch). Health and emotional states are evaluated, and important indicators such as stress levels and lack of exercise are predicted. Input: Integrated data. Output: Analysis results (evaluation of health and emotional states).

[0743] Step 6:

[0744] The server generates a customized health management plan based on the analysis results. This plan includes specific work plans and rest times and is created for real-time feedback. Input: Analysis results. Output: Health management plan.

[0745] Step 7:

[0746] The server notifies the user of the generated health management plan via their smartphone or wearable device. This notification is done in real time, providing the user with optimal advice. Input: Health management plan. Output: Plan notified to the user.

[0747] Step 8:

[0748] The user inputs the results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and used to generate future plans. Input: Feedback data. Output: Feedback data sent to the server.

[0749] Step 9:

[0750] The server updates the database and AI model based on the collected feedback, continuously improving the system, which allows it to provide more accurate and personalized plans for the user's health and emotional state. Input: Feedback data. Output: Updated database and AI model.

[0751] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0752] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0753] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0754] [Third embodiment]

[0755] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0756] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0757] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0758] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0759] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0760] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0761] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0762] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0763] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0764] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0765] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0766] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0767] The present invention provides a system for centrally collecting, pre-processing, integrating, and analyzing personal health data in real time to provide a customized health management plan. Specific embodiments of the system are described in detail below.

[0768] 1. Collection of User Data

[0769] Users use smartphones or wearable devices to input their daily health data, which includes text data (health diaries and survey responses), audio data (voice memos), image data (scanned health records), and video data (exercise videos, etc.).

[0770] 2. Data Preprocessing

[0771] The device preprocesses the health data sent by the user, specifically adjusting the resolution of image data, converting audio data to text, and cleaning and converting text data into a unified format. This preprocessing makes the data suitable for subsequent integration and analysis.

[0772] 3. Data integration

[0773] The server then integrates the pre-processed data, combining data from different formats into a single unified dataset, allowing individual users' health data to be managed centrally.

[0774] 4. AI-powered analysis

[0775] The server analyzes the integrated health data, using a generative model (e.g., a machine learning algorithm) to assess the user's health status from a variety of data. For example, it determines stress levels and lack of exercise from daily health logs.

[0776] 5. Generate a health management plan

[0777] The server generates a customized health management plan based on the analysis results, which includes specific dietary, exercise, and sleep recommendations, allowing users to receive advice optimized for their health status.

[0778] 6. Notice to Users

[0779] The server then notifies the user of the generated health management plan via their smartphone or wearable device. The notification format is tailored to the user's preferences and the characteristics of the device, and may include push notifications for the mobile app or alerts for the wearable device.

[0780] 7. Gathering Feedback

[0781] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0782] Specific examples

[0783] For example, if a user records their diet on a smartphone app and measures their exercise volume with a wearable device, this data is preprocessed on the device and sent to a server. The server then integrates this data and evaluates the user's health using an AI model. Based on the evaluation results, a specific plan is generated, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, allowing future plans to be further personalized.

[0784] The above is a specific embodiment of the present invention, which makes it possible to effectively and efficiently support the health management of a user.

[0785] The processing flow will be explained below.

[0786] Step 1:

[0787] Users use smartphones or wearable devices to input or collect health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of the day's meals into a smartphone app and measure the number of steps taken and heart rate with a smartwatch.

[0788] Step 2:

[0789] The device receives the collected data and performs preprocessing. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes the content to extract important indicators such as sleep time and exercise volume.

[0790] Step 3:

[0791] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0792] Step 4:

[0793] The server analyzes the integrated data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it may determine that the user is lacking exercise based on data from the past week.

[0794] Step 5:

[0795] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0796] Step 6:

[0797] The server notifies the user of the generated health management plan. The plan is communicated to the user using push notifications on the smartphone app, emails, and alerts on wearable devices. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes."

[0798] Step 7:

[0799] The user carries out the health management plan they receive. They then input their results and impressions into the smartphone app as feedback. For example, they can record in the app that they "walked for 30 minutes today" and enter comments about their physical condition and impressions during the walk.

[0800] Step 8:

[0801] The server collects feedback from users and stores it in a database. This feedback data is used to improve the accuracy of the next health management plan. For example, if a user gives feedback that "30 minutes of walking is too strenuous," the server will suggest a 15-minute walk next time.

[0802] The above processing steps realize a system that efficiently manages a user's health data and provides an optimal health management plan according to the individual health condition.

[0803] Example 1

[0804] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0805] In modern society, personal health management is extremely important, and there is a demand for providing appropriate health plans based on various data. However, existing health management systems have difficulty efficiently collecting, integrating, and analyzing data from multiple data sources, making it difficult to provide customized plans tailored to users in real time. Furthermore, the lack of a function to improve plans based on user feedback has limited the effectiveness of long-term health management.

[0806] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0807] In this invention, the server includes means for centrally collecting personal health data, means for preprocessing the health data, means for integrating the preprocessed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for notifying the user of the health management plan, and means for collecting feedback from the user and reflecting the feedback in the next plan, thereby making it possible to provide an optimal health management plan for each individual user and to continuously improve it.

[0808] "Personal health data" refers to data about a user's daily life, such as their heart rate, amount of exercise, diet, and stress level.

[0809] "Preprocessing" refers to the process of converting raw data collected from users into a format that is easier to analyze, and specifically includes adjusting resolution, converting speech to text, and cleaning the data.

[0810] "Integration" refers to the process of combining datasets of different formats into one unified dataset.

[0811] "Real-time analysis" refers to the process of instantly analyzing collected data and reflecting the results immediately.

[0812] "Customized Health Plan" refers to a health plan that includes specific recommended dietary, exercise, sleep, and other actions based on a user's individual data.

[0813] "Feedback" refers to information that the user re-enters into the system regarding the results and impressions of the health management plan that he or she has implemented.

[0814] A "machine learning algorithm" refers to a set of computational techniques that allow a computer to analyze data, recognize patterns, and make predictions.

[0815] The present invention is a system that centrally collects, pre-processes, integrates, and analyzes personal health data in real time to provide customized health management plans.

[0816] 1. Collection of User Data

[0817] Users input health data using smartphones or wearable devices. This data includes text data, voice data, image data, and video data. For example, a user might record "Today's breakfast is vegetable salad and an omelet" in a smartphone app, and the wearable device measures the amount of exercise. This data is sent to the terminal in real time.

[0818] 2. Data Preprocessing

[0819] The device preprocesses the received health data. For example, it uses image processing software (e.g., OpenCV) to adjust the resolution of the photos and remove noise. It also converts the audio data into text using a speech recognition system (e.g., Google Speech-to-Text API), and uses regular expressions to remove unnecessary characters and spaces. This converts the data into a format suitable for analysis.

[0820] 3. Data integration

[0821] The server receives the preprocessed data and stores it in a database system (e.g., MySQL). The server then combines data sets of different formats into a unified data set. This process uses data mapping, for example, to link text data, image data, and audio data by user ID.

[0822] 4. AI-powered analysis

[0823] The server analyzes the integrated data using machine learning algorithms (e.g., TensorFlow or PyTorch). The server compares the user's past data with their current data to recognize patterns. For example, it analyzes past health logs to assess lack of exercise and stress levels. In this process, it utilizes generative AI models to assess the user's health status in real time.

[0824] 5. Generate a health management plan

[0825] The server generates a customized health management plan based on the analysis results. For example, based on the analysis results of "lack of exercise" and "insufficient vitamin intake," the server creates a specific action plan such as "recommended yoga three times a week" or "eat fruit every day." It also advises the user on specific meal menus and recommended exercise amounts.

[0826] 6. Notice to Users

[0827] The server notifies the user of the generated health management plan via a smartphone app or wearable device. For example, it can send a push notification to the smartphone app to inform the user of the new health plan. It can also notify the wearable device via vibration or an alert.

[0828] 7. Gathering Feedback

[0829] Users input the results and impressions of the health management plan they have completed as feedback into the smartphone app. This feedback data is sent back to the server, which analyzes it and uses it to improve the next health management plan. For example, more personalized suggestions can be made based on feedback such as "I like yoga, so I want to continue."

[0830] Specific examples

[0831] For example, if a user records in a smartphone app that "this morning's breakfast was a vegetable salad and an omelet," and then uses a wearable device to measure that "I was sedentary all day," this data is preprocessed on the device and sent to a server. The server then integrates this data and uses an AI model to evaluate the user's health status. Based on the analysis results, a specific health management plan is generated, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to increase exercise." This plan is notified to the user via the smartphone app, and subsequent plans are further personalized based on feedback from the user on the results of their actual implementation of the plan.

[0832] Prompt Sentence Examples

[0833] Below is an example of a prompt sentence to input to the generative AI model.

[0834] "Today's diet was mostly vegetables, but I didn't exercise much. Please provide me with the optimal health plan based on this data."

[0835] "Recommend a health management plan for next week based on your stress levels and exercise habits over the past week."

[0836] In this way, this system efficiently collects and analyzes personal health data and provides users with optimal health plans, thereby supporting effective health management.

[0837] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0838] Step 1: Collect data

[0839] Users input their health data using a smartphone app or wearable device. The data input includes dietary details, exercise volume, heart rate, stress level, etc. For example, a user might input "I had a vegetable salad and an omelet for breakfast," and the wearable device would measure "I was sitting all day." This data is sent to the terminal.

[0840] Input: User's health data (text data, image data, audio data, video data)

[0841] Output: Data sent to the terminal

[0842] Step 2: Preprocessing the data

[0843] The device preprocesses the received health data. Specifically, it uses image processing software (e.g., OpenCV) to adjust the resolution of the image data and remove noise. It also converts the voice data into text data using a voice recognition system (e.g., Google Speech-to-Text API). It then uses regular expressions to remove unnecessary characters and spaces from the text data.

[0844] Input: Raw data collected

[0845] Output: Preprocessed data

[0846] Step 3: Integrate the data

[0847] The server receives the preprocessed data and stores it in a database (e.g., MySQL). The server links different types of data (text data, image data, audio data) by user ID and manages them as a unified data set. Data mapping is used in this process.

[0848] Input: Preprocessed data

[0849] Output: A consolidated dataset

[0850] Step 4: Analyze the data

[0851] The server analyzes the integrated data using machine learning algorithms (e.g., TensorFlow or PyTorch). Specifically, it compares past data with current data to evaluate the user's health status. For example, it analyzes past health logs to evaluate lack of exercise and stress levels. In this process, it utilizes a generative AI model to obtain analysis results in real time.

[0852] Input: Integrated dataset

[0853] Output: Analysis results (user's health status evaluation)

[0854] Step 5: Generate a Health Management Plan

[0855] The server generates a customized health management plan based on the analysis results of the AI ​​model. For example, for a user who is assessed as "not getting enough exercise" or "not getting enough vitamins," it creates a specific action plan such as "recommended yoga three times a week" or "eat one type of fruit every day."

[0856] Input: Analysis results

[0857] Output: A customized health plan

[0858] Step 6: Plan Notification

[0859] The server notifies the user of the generated health management plan via a smartphone app or wearable device. For example, the server can notify the user of the plan by sending a push notification to the smartphone app. The server can also notify the wearable device via vibration or an alert.

[0860] Enter: Customized Health Care Plan

[0861] Output: Notification to user (smartphone app, wearable device)

[0862] Step 7: Gather feedback

[0863] Users provide feedback to the smartphone app about the results and impressions of the health management plan they have completed. This feedback data is then sent back to the server and used to improve the next health management plan. For example, based on feedback such as "I like yoga, so I want to continue," the app will make more personalized suggestions.

[0864] Input: User feedback

[0865] Output: Improved next health plan

[0866] (Application example 1)

[0867] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0868] While systems for personal health management existed in the past, there was a lack of systems that could monitor workers' health status in real time and provide individualized health management plans in specific work environments such as factory environments. Furthermore, there were also insufficient systems that could effectively collect worker health data, analyze it immediately, and enable managers to review it. While this led to a demand for improved work efficiency and safety, workers' health management was not adequately managed, resulting in risks such as reduced productivity and health damage.

[0869] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0870] In this invention, the server includes a means for centrally managing data on the health conditions of individuals, a means for preprocessing the data on the health conditions, and a means for integrating the preprocessed data, thereby enabling real-time monitoring of the health conditions of workers in a factory environment and transmitting the monitoring results to the server for managerial confirmation.

[0871] "Personal health data" refers to health-related data such as a user's heart rate, number of steps, calories burned, and sleep time.

[0872] "Preprocessing" refers to the process of converting collected data into a form suitable for data analysis, such as adjusting the resolution of the data, converting audio data into text, and cleaning text data.

[0873] "Integration" is the process of bringing together different types of health data into a single unified dataset.

[0874] "Instant analysis" is the process of analyzing collected data in real time to assess the user's health status.

[0875] A "personalized health management action plan" is a plan that includes specific recommended dietary, exercise, and sleep actions based on the user's health status.

[0876] "User" refers to an individual who provides health data and receives a health management action plan based on that data.

[0877] "Monitoring" refers to the act of monitoring a user's health status in real time.

[0878] A "server" is a computer system for centralized management, preprocessing, integration, and analysis of data.

[0879] "Supervisor" refers to a person in a factory environment who is responsible for monitoring the health of workers and determining appropriate responses.

[0880] "Workers" refers to people who work in factories or on work sites.

[0881] "Factory environment" refers to the entire facility and surrounding environment used for manufacturing and production.

[0882] The present invention is a system for real-time monitoring of worker health in a factory environment and providing personalized health management action plans. This system centralizes and instantly analyzes data on individual health conditions, resulting in an efficient work environment.

[0883] User Data Collection

[0884] The users, or workers, collect health data such as heart rate, number of steps, calories burned, and sleep time from their smartphones or wearable devices, and this data is sent to a server in real time via the devices.

[0885] Data Preprocessing

[0886] The server preprocesses the health data sent by the user, adjusting the data resolution, converting voice data to text, cleaning the text data, and other processes to prepare the data for analysis.

[0887] Data integration

[0888] The server then integrates the pre-processed data, bringing together different forms of health data into a single unified dataset for efficient data analysis.

[0889] AI-powered analysis

[0890] The server analyzes the integrated health data in real time, using a generative AI model to instantly assess the user's health status, for example, determining a worker's stress level or lack of exercise based on their daily health log.

[0891] Generate a health management plan

[0892] The server then generates a personalized health management action plan based on the analysis results, including specific dietary, exercise, and sleep recommendations, such as "eat fruit daily to increase your vitamin intake" or "do yoga three times a week to reduce stress."

[0893] User Notification

[0894] The server then notifies the user of the generated health management plan via their smartphone or wearable device. The notification format is tailored to the user's preferences and device characteristics, and can include push notifications for the mobile app or alerts for the wearable device.

[0895] Gathering feedback

[0896] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0897] Specific examples

[0898] For example, if a worker records their diet on a smartphone app and measures their exercise volume with a wearable device, this data is preprocessed on the device and sent to a server. The server then integrates this data and evaluates the user's health using an AI model. Based on the evaluation results, a specific plan is generated, such as "eat fruit daily to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the results of the user's actual implementation of the plan can be input as feedback, allowing future plans to be further personalized.

[0899] Example of a generative AI model prompt:

[0900] "If a worker appears to be in a high-stress state after eight hours of work and his heart rate is higher than normal, suggest appropriate health management actions, such as taking appropriate breaks and exercising to reduce stress."

[0901] The above is a specific embodiment of the present invention, which makes it possible to effectively and efficiently support the health management of workers in a factory environment.

[0902] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0903] Step 1: Collecting personal health data

[0904] Users collect health data such as heart rate, steps, calories burned, and sleep time from their smartphones or wearable devices. This data may be entered manually by the user or automatically recorded by the device.

[0905] Input: Health data such as heart rate, steps, calories burned, and sleep time

[0906] Output: Data collected by smartphones and wearable devices

[0907] Step 2: Preprocessing the data

[0908] The device preprocesses the health data sent by the user, including adjusting the resolution of the data, converting voice data to text, and cleaning the text data, so that the data is in a form suitable for analysis on the server.

[0909] Input: Raw data collected

[0910] Output: Preprocessed data

[0911] Step 3: Integrate the data

[0912] The server then integrates the preprocessed data, combining data of different formats (text, audio, images, video, etc.) into a single unified dataset. Specific operations include changing the order in which the data is viewed and standardizing the format.

[0913] Input: Preprocessed data

[0914] Output: A consolidated dataset

[0915] Step 4: AI analysis

[0916] The server analyzes the integrated health data in real time, using a machine learning generative model to assess the user's health status from the data. For example, if the user's heart rate is high, it can determine whether the user is showing signs of dehydration or stress.

[0917] Input: Integrated dataset

[0918] Output: Health status assessment results

[0919] Step 5: Generate a Health Management Plan

[0920] The server then generates a personalized health management action plan based on the analysis results, with specific recommendations such as "eat fruit daily to increase your vitamin intake" or "do yoga three times a week to reduce stress."

[0921] Input: Health status assessment results

[0922] Output: personalized health care plan

[0923] Step 6: Notify users

[0924] The server then sends the generated health management plan to the user's smartphone or wearable device, which then sends a push notification or alert.

[0925] Enter: personalized health care plans.

[0926] Output: Plan notified to user's device

[0927] Step 7: Gather feedback

[0928] The user inputs the results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and is used to generate future plans.

[0929] Input: User feedback

[0930] Output: Data used to generate improved plans

[0931] The above are the specific processing steps of the system program of the present invention.

[0932] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0933] The present invention combines a system that centrally collects, pre-processes, integrates, and analyzes personal health data in real time to provide a customized health management plan, with an emotion engine that recognizes the user's emotions. Specific embodiments of the present invention will be described in detail below.

[0934] 1. Collection of User Data

[0935] Users use smartphones or wearable devices to input or collect daily health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of their daily meals into a smartphone app and measure their steps and heart rate with a smartwatch.

[0936] 2. Data Preprocessing

[0937] The device preprocesses the health data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes their contents to extract important indicators such as sleep duration and exercise volume.

[0938] 3. Data integration

[0939] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0940] 4. AI-powered analysis

[0941] The server analyzes the integrated health data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it may determine that the user is lacking exercise based on data from the past week.

[0942] 5. Generate a health management plan

[0943] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0944] 6. Emotion Recognition by Emotion Engine

[0945] The server uses the voice and image data provided by the user to recognize the user's emotions using an emotion engine. Specifically, it analyzes voice tone and facial expressions to estimate emotional states such as stress, happiness, and fatigue. For example, it can analyze the user's voice tone and choice of words from their voice memo to estimate that their stress level is high.

[0946] 7. Adjust your plan based on your emotional state

[0947] The server adjusts the generated health management plan based on the user's emotional state as recognized by the emotion engine: for example, if the user is feeling stressed, relaxation and stress relief techniques are added to the plan.

[0948] 8. Notice to Users

[0949] The server notifies the user of the generated and adjusted health management plan via their smartphone or wearable device. The notification format is adjusted according to the user's preferences and the characteristics of the device. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes and have a relaxing massage in the evening."

[0950] 9. Collecting Feedback

[0951] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[0952] Specific examples

[0953] For example, if a user records their diet on a smartphone app and measures their exercise volume with a wearable device, these data are preprocessed on the device and sent to a server. Furthermore, if the user adds a voice memo to the app, the emotion engine analyzes the voice and recognizes that the user is feeling stressed. The server integrates these data, evaluates the user's health condition using an AI model, and, taking into account the results of the emotion engine, generates a specific health management plan, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, which will further personalize future plans.

[0954] The above is a specific embodiment of the present invention. The present invention effectively and efficiently supports the user's health management and also makes it possible to provide an optimal plan according to the user's emotional state.

[0955] The processing flow will be explained below.

[0956] Step 1:

[0957] Users input or collect daily health data using smartphones or wearable devices. This data includes dietary information entered into a smartphone app, steps taken and heart rate measured by a smartwatch, and voice memos recording impressions and physical condition.

[0958] Step 2:

[0959] The device preprocesses various data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also cleans the text data and removes unnecessary information. For example, it transcribes voice memos and extracts health-related keywords from their content.

[0960] Step 3:

[0961] The server centrally integrates the preprocessed data. Data collected from different devices is aggregated for each user and managed as a single dataset. For example, food logs from a smartphone, step count data from a smartwatch, and gratitude keywords extracted from voice memos can all be aggregated.

[0962] Step 4:

[0963] The server analyzes the integrated health data, using generative models and machine learning algorithms to assess the user's health status, for example, by analyzing the amount of exercise, diet, and sleep patterns over the past week to determine whether the user is physically inactive.

[0964] Step 5:

[0965] The server uses an emotion engine to recognize the user's emotional state based on the user's voice data and image data. For example, it analyzes the tone of the voice memo and facial expressions in the image to determine whether the user is feeling stressed.

[0966] Step 6:

[0967] The server generates a customized health management plan based on the analysis results and the user's emotional state. The plan includes recommended actions to improve the user's health, such as diet, exercise, and sleep. For example, a user who is feeling stressed due to lack of exercise might be advised to "walk 30 minutes every day and do yoga three times a week."

[0968] Step 7:

[0969] The server notifies the user of the generated health management plan. The user can check the plan through push notifications on the smartphone app, emails, alerts on wearable devices, etc. For example, a notification on the smartphone may read, "Today's health management plan: 30 minutes of walking and a relaxing massage in the evening."

[0970] Step 8:

[0971] The user carries out the health management plan they receive. They then input their results and impressions as feedback into the smartphone app. For example, they might record things like, "I walked for 30 minutes today and also had a relaxing massage," or "I felt relaxed after the massage."

[0972] Step 9:

[0973] The server collects user feedback and stores it in a database. Based on this feedback, the next health plan can be further personalized, and the user's health status can be continuously monitored and improved. For example, based on the feedback, the next plan may adjust the walking time or suggest different relaxation methods.

[0974] The above processing steps realize a system that comprehensively manages a user's health data and provides an optimal health management plan that takes into account the user's emotional state.

[0975] Example 2

[0976] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0977] Modern health management systems are capable of collecting and centrally managing personal health data. However, these systems often lack the ability to analyze data in real time and generate personalized health management plans. Furthermore, they lack the ability to provide health management plans that take into account the user's emotional state. Therefore, there is a need for a system that can collect, preprocess, integrate, and analyze personal health and emotional data in real time and provide customized health management plans that take into account the user's emotional state.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0979] In this invention, the server includes means for centrally collecting personal health data, means for preprocessing the health data, means for integrating the preprocessed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for notifying the user of the health management plan, means for collecting and analyzing emotional data of the user, and means for adjusting the health management plan based on the emotional data. This makes it possible to monitor the health status of an individual in detail in real time and provide an optimal health management plan according to the emotional state.

[0980] "Means for collection" refers to the method or device that allows users to input daily health data using a smartphone or wearable device and have it imported into the system.

[0981] "Preprocessing means" refers to methods or devices that organize and transform collected health data into a format suitable for analysis, such as standardizing the resolution of image data or converting audio data into text.

[0982] The "integrating means" refers to a method or device for consolidating the preprocessed data, associating it with each user, and storing it in a database.

[0983] "Real-time analysis means" means a method or device for analyzing the integrated data in real time to assess the user's health status, including using generative models or machine learning algorithms.

[0984] A "means for generating a healthcare plan" is a method or device for creating an individualized healthcare plan based on the results of real-time analysis.

[0985] The "means for notifying the user" refers to a method or device for transmitting the generated health management plan to the user's smartphone or wearable device to notify the user.

[0986] "Means for collecting and analyzing emotional data" refers to a method or device for recognizing and analyzing the emotional state of a user using voice data or image data provided by the user.

[0987] "Means for adjusting a healthcare plan based on emotional data" refers to a method or apparatus for further optimizing or modifying an already generated healthcare plan based on a recognized emotional state.

[0988] This invention is a system that centrally collects, preprocesses, integrates, and analyzes personal health and emotional data in real time to generate and provide a customized health management plan. This system also takes into account the user's emotional state, providing more personalized health management.

[0989] User Data Collection

[0990] Users use smartphones or wearable devices to input or collect daily health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of their daily meals into a smartphone app and measure their steps and heart rate with a smartwatch.

[0991] Data Preprocessing

[0992] The device preprocesses the health data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes their contents to extract important indicators such as sleep duration and exercise volume.

[0993] Data integration

[0994] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[0995] AI-powered analysis

[0996] The server analyzes the integrated health data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it determines that the user is lacking exercise based on data from the past week.

[0997] Generate a health management plan

[0998] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[0999] Emotion recognition by emotion engine

[1000] The server uses the voice and image data provided by the user to recognize the user's emotions using an emotion engine. Specifically, it analyzes voice tone and facial expressions to estimate emotional states such as stress, happiness, and fatigue. For example, it can analyze the user's voice tone and choice of words from their voice memo to estimate that their stress level is high.

[1001] Adjusting your plan based on your emotional state

[1002] The server adjusts the generated health management plan based on the user's emotional state as recognized by the emotion engine: for example, if the user is feeling stressed, relaxation and stress relief techniques are added to the plan.

[1003] User Notification

[1004] The server then sends the generated and adjusted health management plan to the user's smartphone or wearable device. The notification format is adjusted according to the user's preferences and the characteristics of the device. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes and have a relaxing massage in the evening."

[1005] Gathering feedback

[1006] Users input their results and impressions of the health management plan they have implemented as feedback. The server receives this feedback and uses it to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[1007] Specific examples

[1008] For example, if a user records their diet in a smartphone app and measures their exercise volume with a wearable device, these data are preprocessed on the device and sent to a server. Furthermore, if the user adds a voice memo to the app, the emotion engine analyzes the voice and recognizes that the user is feeling stressed. The server integrates these data, evaluates the user's health status using a generative model, and, taking into account the results of the emotion engine, generates a specific health management plan, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, which will further personalize future plans.

[1009] Example prompts to be input to the generative AI model

[1010] "Could you please give me some advice on diet management? My current diet consists of oatmeal for breakfast, salad for lunch, and pasta for dinner."

[1011] "I feel like I haven't been getting enough exercise lately. Please tell me a specific exercise plan."

[1012] "My stress levels are high. How can I relax?"

[1013] "Generate a health plan for this week. I've already entered my daily steps, meals, and sleep."

[1014] The above is a specific embodiment of the present invention. The present invention effectively and efficiently supports the user's health management and also makes it possible to provide an optimal plan according to the user's emotional state.

[1015] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1016] Step 1:

[1017] Users collect health data using smartphones and wearable devices.

[1018] Specific operation: The user enters the details of their meal into a smartphone app, and the wearable device measures their exercise volume and heart rate.

[1019] Input: Text data and sensor data on diet, exercise, and sleep.

[1020] Output: Collected health data.

[1021] Step 2:

[1022] The device preprocesses the collected health data.

[1023] Specific operations: Image data is resized to 500x500 pixels, voice memos are converted to text using voice recognition software, and the text data is analyzed to extract key information.

[1024] Input: Collected health data (text, images, audio, etc.).

[1025] Output: Preprocessed health data (standardized images, transcribed audio, parsed text).

[1026] Step 3:

[1027] The server consolidates the pre-processed data.

[1028] Specific operation: Meal data from the smartphone and exercise data from the wearable device are associated with the user ID and stored in a centralized database.

[1029] Input: Preprocessed health data.

[1030] Output: Integrated health database.

[1031] Step 4:

[1032] The server analyzes the integrated data in real time using generative AI models.

[1033] What it does: Input the integrated data into an analytical model to calculate stress levels and physical inactivity scores.

[1034] Input: Integrated Health Database.

[1035] Output: User's health assessment (stress level, physical inactivity score, etc.).

[1036] Step 5:

[1037] The server generates a customized health care plan based on the analysis results.

[1038] Specific actions: Based on the analysis results, specific instructions such as "walk 30 minutes every day" and "eat foods rich in vitamin D" are created.

[1039] Input: User's health assessment.

[1040] Output: A customized health care plan.

[1041] Step 6:

[1042] The server collects and analyzes the emotion data.

[1043] Specific operation: Using voice and image data provided by the user, the system analyzes voice tone and facial expressions to recognize the user's emotional state.

[1044] Input: Audio data, image data.

[1045] Output: The user's emotional state (stress, happiness, fatigue, etc.).

[1046] Step 7:

[1047] The server adjusts the health management plan based on the emotion data.

[1048] Specific behavior: If the user's stress level is high, add relaxation and stress relief actions to the health management plan.

[1049] Input: User's emotional state, customized health care plan.

[1050] Output: An adjusted health care plan that takes into account your emotional state.

[1051] Step 8:

[1052] The server notifies the user of the generated and adjusted health care plan.

[1053] Specific operation: A push notification is sent to the smartphone, informing the user of the health management plan for today: 30 minutes of walking and a relaxing massage in the evening.

[1054] Enter: Coordinated Health Care Plan.

[1055] Output: A notification message to the user.

[1056] Step 9:

[1057] The user inputs the results and impressions of the health management plan they have implemented as feedback. The server receives this feedback and reflects it in the generation of subsequent plans.

[1058] Specific operation: The user writes in the smartphone app, "I walked for 30 minutes today" and "I felt good," and the server reflects this information in the next plan.

[1059] Input: User feedback.

[1060] Output: Feedback data reflected in future plans.

[1061] (Application example 2)

[1062] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1063] In recent years, personal health management has become increasingly important, with a particular need for real-time monitoring of health status and appropriate feedback. However, conventional systems have limitations in collecting and analyzing health data and providing personalized health plans, making it difficult to simultaneously manage workers' health and emotional states, particularly in the workplace. Furthermore, health management plans are not appropriately adjusted according to work conditions and emotions, making it difficult to achieve sufficient results in improving work efficiency and maintaining health.

[1064] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for centrally collecting personal health data, means for pre-processing the health data, means for integrating the pre-processed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for collecting emotional data, means for analyzing the emotional data, means for adjusting the health management plan based on the analyzed emotional data, and means for notifying the user of the health management plan. This makes it possible to simultaneously manage the health state and emotional state of a worker and propose optimal work plans or break times in real time.

[1065] "Personal health data" refers to information such as heart rate, steps taken, and exercise volume collected through devices such as smartwatches and smartphones.

[1066] "Preprocessing" refers to the process of extracting necessary information from collected raw data using techniques such as syntax analysis and standardization, thereby improving the quality of the data.

[1067] "Integration" refers to aggregating preprocessed data into a centralized database such as a cloud server and managing it as a unified data set.

[1068] "Real-time analysis" refers to the immediate assessment of the current state based on collected and integrated data, including analysis using generative models and machine learning algorithms.

[1069] A "customized health management plan" refers to a plan that provides specific advice on optimal diet, exercise, rest, etc. based on an individual's health and emotional data.

[1070] "Emotional data" refers to information about a user's emotional state analyzed by the emotion engine using voice, facial expressions, etc.

[1071] An "emotion engine" is a system that analyzes voice tone and facial expressions to estimate a user's emotional state, such as stress, happiness, or fatigue.

[1072] "Adjusting a health management plan" refers to optimizing and modifying an existing health management plan based on the analyzed emotional data.

[1073] "Notification" refers to the act of informing the user of a generated or adjusted health management plan by sending a notification to a smartphone or wearable device.

[1074] "Suggesting work plans or break times" means providing workers with optimal work progress methods and appropriate break times based on health and emotional data.

[1075] The present invention relates to a system that manages and analyzes the health and emotional state of factory workers in real time and provides optimal work plans and break times.

[1076] Health data collection

[1077] Users use smartwatches or smartphones to collect and input daily health data such as heart rate, steps taken, and exercise volume, including data automatically collected through wearable devices.

[1078] Collecting Emotional Data

[1079] The device (e.g., a robot in a factory) collects the voice and facial expressions of the worker using CCTV cameras and audio microphones, and then uses an emotion engine to recognize the user's emotional state and estimate their state of stress, happiness, fatigue, etc. in real time.

[1080] Data Preprocessing

[1081] The collected health and emotion data is preprocessed by the device's on-board computer, which converts voice data into text, standardizes the resolution of image data, and removes unnecessary information through syntactic analysis to extract key indicators.

[1082] Data integration

[1083] The pre-processed data is sent to a cloud server and integrated into a centralized database. By centrally managing health and emotion data for each user, subsequent analysis processes can be carried out efficiently.

[1084] AI-powered analysis

[1085] The server analyzes the integrated data in real time using AI models (e.g., generative models such as TensorFlow or PyTorch) to assess the health and emotional state of workers and predict key health indicators such as stress levels and lack of exercise.

[1086] Generate a health management plan

[1087] The server generates a customized health management plan based on the analysis results. This plan takes into account the worker's health and emotional state and provides specific recommendations for optimal work schedules and break times. For example, it may include specific instructions such as "Take 10 minutes of relaxation time" or "Take a 5-minute break for deep breathing."

[1088] User Notification

[1089] The generated health management plan is sent from the server to the user via their device, such as a smartphone or wearable device, in real time.

[1090] Gathering feedback

[1091] Users input their results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and used to generate future plans. The system continuously monitors the user's health and emotional state and supports improvement.

[1092] Specific examples

[1093] For example, heart rate and step count data collected by the user's smartwatch is sent to a cloud server, while the robot's CCTV camera analyzes the worker's facial expressions in real time to assess their stress level. As a result, the server generates a health management plan such as "Take 10 minutes to relax," and notifies the worker via their smartphone.

[1094] Prompt Sentence Examples

[1095] "Recommend optimal break plans based on workers' health and emotional data. Health data includes heart rate and number of steps. Emotion data includes emotion labels based on facial expressions."

[1096] The present invention makes it possible to simultaneously manage the health and emotional state of factory workers and provide appropriate feedback in real time, thereby effectively supporting work efficiency and worker health.

[1097] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1098] Step 1:

[1099] Users collect and input health data using a smartwatch or smartphone. This data includes heart rate, number of steps, and amount of exercise. The collected data is sent from the wearable device to the smartphone. Input: Heart rate, number of steps, amount of exercise. Output: Health data transferred to the device.

[1100] Step 2:

[1101] The terminal collects the voices and facial expressions of workers using CCTV cameras and audio microphones. This includes collecting voice data and image data in real time. Input: Voice data, facial expression data. Output: Emotion data collected in real time.

[1102] Step 3:

[1103] The device preprocesses the collected health and emotion data. Voice data is converted to text (for example, using voice recognition software), and image data has its resolution standardized. It also removes unnecessary information from the collected data and extracts important information. Input: Voice data, image data. Output: Preprocessed health and emotion data.

[1104] Step 4:

[1105] The preprocessed data is sent from the device to a cloud server and integrated into a centralized database. This allows the data to be organized by user. Input: Preprocessed health data and emotion data. Output: Integrated data on the cloud server.

[1106] Step 5:

[1107] The server analyzes the integrated data in real time using an AI model (e.g., a generative model such as TensorFlow or PyTorch). Health and emotional states are evaluated, and important indicators such as stress levels and lack of exercise are predicted. Input: Integrated data. Output: Analysis results (evaluation of health and emotional states).

[1108] Step 6:

[1109] The server generates a customized health management plan based on the analysis results. This plan includes specific work plans and rest times and is created for real-time feedback. Input: Analysis results. Output: Health management plan.

[1110] Step 7:

[1111] The server notifies the user of the generated health management plan via their smartphone or wearable device. This notification is done in real time, providing the user with optimal advice. Input: Health management plan. Output: Plan notified to the user.

[1112] Step 8:

[1113] The user inputs the results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and used to generate future plans. Input: Feedback data. Output: Feedback data sent to the server.

[1114] Step 9:

[1115] The server updates the database and AI model based on the collected feedback, continuously improving the system, which allows it to provide more accurate and personalized plans for the user's health and emotional state. Input: Feedback data. Output: Updated database and AI model.

[1116] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1118] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1119] [Fourth embodiment]

[1120] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1121] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1123] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1124] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1127] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1128] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1129] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1131] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1132] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1133] The present invention provides a system for centrally collecting, pre-processing, integrating, and analyzing personal health data in real time to provide a customized health management plan. Specific embodiments of the system are described in detail below.

[1134] 1. Collection of User Data

[1135] Users use smartphones or wearable devices to input their daily health data, which includes text data (health diaries and survey responses), audio data (voice memos), image data (scanned health records), and video data (exercise videos, etc.).

[1136] 2. Data Preprocessing

[1137] The device preprocesses the health data sent by the user, specifically adjusting the resolution of image data, converting audio data to text, and cleaning and converting text data into a unified format. This preprocessing makes the data suitable for subsequent integration and analysis.

[1138] 3. Data integration

[1139] The server then integrates the pre-processed data, combining data from different formats into a single unified dataset, allowing individual users' health data to be managed centrally.

[1140] 4. AI-powered analysis

[1141] The server analyzes the integrated health data, using a generative model (e.g., a machine learning algorithm) to assess the user's health status from a variety of data. For example, it determines stress levels and lack of exercise from daily health logs.

[1142] 5. Generate a health management plan

[1143] The server generates a customized health management plan based on the analysis results, which includes specific dietary, exercise, and sleep recommendations, allowing users to receive advice optimized for their health status.

[1144] 6. Notice to Users

[1145] The server then notifies the user of the generated health management plan via their smartphone or wearable device. The notification format is tailored to the user's preferences and the characteristics of the device, and may include push notifications for the mobile app or alerts for the wearable device.

[1146] 7. Gathering Feedback

[1147] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[1148] Specific examples

[1149] For example, if a user records their diet on a smartphone app and measures their exercise volume with a wearable device, this data is preprocessed on the device and sent to a server. The server then integrates this data and evaluates the user's health using an AI model. Based on the evaluation results, a specific plan is generated, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, allowing future plans to be further personalized.

[1150] The above is a specific embodiment of the present invention, which makes it possible to effectively and efficiently support the health management of a user.

[1151] The processing flow will be explained below.

[1152] Step 1:

[1153] Users use smartphones or wearable devices to input or collect health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of the day's meals into a smartphone app and measure the number of steps taken and heart rate with a smartwatch.

[1154] Step 2:

[1155] The device receives the collected data and performs preprocessing. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes the content to extract important indicators such as sleep time and exercise volume.

[1156] Step 3:

[1157] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[1158] Step 4:

[1159] The server analyzes the integrated data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it may determine that the user is lacking exercise based on data from the past week.

[1160] Step 5:

[1161] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[1162] Step 6:

[1163] The server notifies the user of the generated health management plan. The plan is communicated to the user using push notifications on the smartphone app, emails, and alerts on wearable devices. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes."

[1164] Step 7:

[1165] The user carries out the health management plan they receive. They then input their results and impressions into the smartphone app as feedback. For example, they can record in the app that they "walked for 30 minutes today" and enter comments about their physical condition and impressions during the walk.

[1166] Step 8:

[1167] The server collects feedback from users and stores it in a database. This feedback data is used to improve the accuracy of the next health management plan. For example, if a user gives feedback that "30 minutes of walking is too strenuous," the server will suggest a 15-minute walk next time.

[1168] The above processing steps realize a system that efficiently manages a user's health data and provides an optimal health management plan according to the individual health condition.

[1169] Example 1

[1170] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1171] In modern society, personal health management is extremely important, and there is a demand for providing appropriate health plans based on various data. However, existing health management systems have difficulty efficiently collecting, integrating, and analyzing data from multiple data sources, making it difficult to provide customized plans tailored to users in real time. Furthermore, the lack of a function to improve plans based on user feedback has limited the effectiveness of long-term health management.

[1172] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1173] In this invention, the server includes means for centrally collecting personal health data, means for preprocessing the health data, means for integrating the preprocessed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for notifying the user of the health management plan, and means for collecting feedback from the user and reflecting the feedback in the next plan, thereby making it possible to provide an optimal health management plan for each individual user and to continuously improve it.

[1174] "Personal health data" refers to data about a user's daily life, such as their heart rate, amount of exercise, diet, and stress level.

[1175] "Preprocessing" refers to the process of converting raw data collected from users into a format that is easier to analyze, and specifically includes adjusting resolution, converting speech to text, and cleaning the data.

[1176] "Integration" refers to the process of combining datasets of different formats into one unified dataset.

[1177] "Real-time analysis" refers to the process of instantly analyzing collected data and reflecting the results immediately.

[1178] "Customized Health Plan" refers to a health plan that includes specific recommended dietary, exercise, sleep, and other actions based on a user's individual data.

[1179] "Feedback" refers to information that the user re-enters into the system regarding the results and impressions of the health management plan that he or she has implemented.

[1180] A "machine learning algorithm" refers to a set of computational techniques that allow a computer to analyze data, recognize patterns, and make predictions.

[1181] The present invention is a system that centrally collects, pre-processes, integrates, and analyzes personal health data in real time to provide customized health management plans.

[1182] 1. Collection of User Data

[1183] Users input health data using smartphones or wearable devices. This data includes text data, voice data, image data, and video data. For example, a user might record "Today's breakfast is vegetable salad and an omelet" in a smartphone app, and the wearable device measures the amount of exercise. This data is sent to the terminal in real time.

[1184] 2. Data Preprocessing

[1185] The device preprocesses the received health data. For example, it uses image processing software (e.g., OpenCV) to adjust the resolution of the photos and remove noise. It also converts the audio data into text using a speech recognition system (e.g., Google Speech-to-Text API), and uses regular expressions to remove unnecessary characters and spaces. This converts the data into a format suitable for analysis.

[1186] 3. Data integration

[1187] The server receives the preprocessed data and stores it in a database system (e.g., MySQL). The server then combines data sets of different formats into a unified data set. This process uses data mapping, for example, to link text data, image data, and audio data by user ID.

[1188] 4. AI-powered analysis

[1189] The server analyzes the integrated data using machine learning algorithms (e.g., TensorFlow or PyTorch). The server compares the user's past data with their current data to recognize patterns. For example, it analyzes past health logs to assess lack of exercise and stress levels. In this process, it utilizes generative AI models to assess the user's health status in real time.

[1190] 5. Generate a health management plan

[1191] The server generates a customized health management plan based on the analysis results. For example, based on the analysis results of "lack of exercise" and "insufficient vitamin intake," the server creates a specific action plan such as "recommended yoga three times a week" or "eat fruit every day." It also advises the user on specific meal menus and recommended exercise amounts.

[1192] 6. Notice to Users

[1193] The server notifies the user of the generated health management plan via a smartphone app or wearable device. For example, it can send a push notification to the smartphone app to inform the user of the new health plan. It can also notify the wearable device via vibration or an alert.

[1194] 7. Gathering Feedback

[1195] Users input the results and impressions of the health management plan they have completed as feedback into the smartphone app. This feedback data is sent back to the server, which analyzes it and uses it to improve the next health management plan. For example, more personalized suggestions can be made based on feedback such as "I like yoga, so I want to continue."

[1196] Specific examples

[1197] For example, if a user records in a smartphone app that "this morning's breakfast was a vegetable salad and an omelet," and then uses a wearable device to measure that "I was sedentary all day," this data is preprocessed on the device and sent to a server. The server then integrates this data and uses an AI model to evaluate the user's health status. Based on the analysis results, a specific health management plan is generated, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to increase exercise." This plan is notified to the user via the smartphone app, and subsequent plans are further personalized based on feedback from the user on the results of their actual implementation of the plan.

[1198] Prompt Sentence Examples

[1199] Below is an example of a prompt sentence to input to the generative AI model.

[1200] "Today's diet was mostly vegetables, but I didn't exercise much. Please provide me with the optimal health plan based on this data."

[1201] "Recommend a health management plan for next week based on your stress levels and exercise habits over the past week."

[1202] In this way, this system efficiently collects and analyzes personal health data and provides users with optimal health plans, thereby supporting effective health management.

[1203] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1204] Step 1: Collect data

[1205] Users input their health data using a smartphone app or wearable device. The data input includes dietary details, exercise volume, heart rate, stress level, etc. For example, a user might input "I had a vegetable salad and an omelet for breakfast," and the wearable device would measure "I was sitting all day." This data is sent to the terminal.

[1206] Input: User's health data (text data, image data, audio data, video data)

[1207] Output: Data sent to the terminal

[1208] Step 2: Preprocessing the data

[1209] The device preprocesses the received health data. Specifically, it uses image processing software (e.g., OpenCV) to adjust the resolution of the image data and remove noise. It also converts the voice data into text data using a voice recognition system (e.g., Google Speech-to-Text API). It then uses regular expressions to remove unnecessary characters and spaces from the text data.

[1210] Input: Raw data collected

[1211] Output: Preprocessed data

[1212] Step 3: Integrate the data

[1213] The server receives the preprocessed data and stores it in a database (e.g., MySQL). The server links different types of data (text data, image data, audio data) by user ID and manages them as a unified data set. Data mapping is used in this process.

[1214] Input: Preprocessed data

[1215] Output: A consolidated dataset

[1216] Step 4: Analyze the data

[1217] The server analyzes the integrated data using machine learning algorithms (e.g., TensorFlow or PyTorch). Specifically, it compares past data with current data to evaluate the user's health status. For example, it analyzes past health logs to evaluate lack of exercise and stress levels. In this process, it utilizes a generative AI model to obtain analysis results in real time.

[1218] Input: Integrated dataset

[1219] Output: Analysis results (user's health status evaluation)

[1220] Step 5: Generate a Health Management Plan

[1221] The server generates a customized health management plan based on the analysis results of the AI ​​model. For example, for a user who is assessed as "not getting enough exercise" or "not getting enough vitamins," it creates a specific action plan such as "recommended yoga three times a week" or "eat one type of fruit every day."

[1222] Input: Analysis results

[1223] Output: A customized health plan

[1224] Step 6: Plan Notification

[1225] The server notifies the user of the generated health management plan via a smartphone app or wearable device. For example, the server can notify the user of the plan by sending a push notification to the smartphone app. The server can also notify the wearable device via vibration or an alert.

[1226] Enter: Customized Health Care Plan

[1227] Output: Notification to user (smartphone app, wearable device)

[1228] Step 7: Gather feedback

[1229] Users provide feedback to the smartphone app about the results and impressions of the health management plan they have completed. This feedback data is then sent back to the server and used to improve the next health management plan. For example, based on feedback such as "I like yoga, so I want to continue," the app will make more personalized suggestions.

[1230] Input: User feedback

[1231] Output: Improved next health plan

[1232] (Application example 1)

[1233] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1234] While systems for personal health management existed in the past, there was a lack of systems that could monitor workers' health status in real time and provide individualized health management plans in specific work environments such as factory environments. Furthermore, there were also insufficient systems that could effectively collect worker health data, analyze it immediately, and enable managers to review it. While this led to a demand for improved work efficiency and safety, workers' health management was not adequately managed, resulting in risks such as reduced productivity and health damage.

[1235] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1236] In this invention, the server includes a means for centrally managing data on the health conditions of individuals, a means for preprocessing the data on the health conditions, and a means for integrating the preprocessed data, thereby enabling real-time monitoring of the health conditions of workers in a factory environment and transmitting the monitoring results to the server for managerial confirmation.

[1237] "Personal health data" refers to health-related data such as a user's heart rate, number of steps, calories burned, and sleep time.

[1238] "Preprocessing" refers to the process of converting collected data into a form suitable for data analysis, such as adjusting the resolution of the data, converting audio data into text, and cleaning text data.

[1239] "Integration" is the process of bringing together different types of health data into a single unified dataset.

[1240] "Instant analysis" is the process of analyzing collected data in real time to assess the user's health status.

[1241] A "personalized health management action plan" is a plan that includes specific recommended dietary, exercise, and sleep actions based on the user's health status.

[1242] "User" refers to an individual who provides health data and receives a health management action plan based on that data.

[1243] "Monitoring" refers to the act of monitoring a user's health status in real time.

[1244] A "server" is a computer system for centralized management, preprocessing, integration, and analysis of data.

[1245] "Supervisor" refers to a person in a factory environment who is responsible for monitoring the health of workers and determining appropriate responses.

[1246] "Workers" refers to people who work in factories or on work sites.

[1247] "Factory environment" refers to the entire facility and surrounding environment used for manufacturing and production.

[1248] The present invention is a system for real-time monitoring of worker health in a factory environment and providing personalized health management action plans. This system centralizes and instantly analyzes data on individual health conditions, resulting in an efficient work environment.

[1249] User Data Collection

[1250] The users, or workers, collect health data such as heart rate, number of steps, calories burned, and sleep time from their smartphones or wearable devices, and this data is sent to a server in real time via the devices.

[1251] Data Preprocessing

[1252] The server preprocesses the health data sent by the user, adjusting the data resolution, converting voice data to text, cleaning the text data, and other processes to prepare the data for analysis.

[1253] Data integration

[1254] The server then integrates the pre-processed data, bringing together different forms of health data into a single unified dataset for efficient data analysis.

[1255] AI-powered analysis

[1256] The server analyzes the integrated health data in real time, using a generative AI model to instantly assess the user's health status, for example, determining a worker's stress level or lack of exercise based on their daily health log.

[1257] Generate a health management plan

[1258] The server then generates a personalized health management action plan based on the analysis results, including specific dietary, exercise, and sleep recommendations, such as "eat fruit daily to increase your vitamin intake" or "do yoga three times a week to reduce stress."

[1259] User Notification

[1260] The server then notifies the user of the generated health management plan via their smartphone or wearable device. The notification format is tailored to the user's preferences and device characteristics, and can include push notifications for the mobile app or alerts for the wearable device.

[1261] Gathering feedback

[1262] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[1263] Specific examples

[1264] For example, if a worker records their diet on a smartphone app and measures their exercise volume with a wearable device, this data is preprocessed on the device and sent to a server. The server then integrates this data and evaluates the user's health using an AI model. Based on the evaluation results, a specific plan is generated, such as "eat fruit daily to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the results of the user's actual implementation of the plan can be input as feedback, allowing future plans to be further personalized.

[1265] Example of a generative AI model prompt:

[1266] "If a worker appears to be in a high-stress state after eight hours of work and his heart rate is higher than normal, suggest appropriate health management actions, such as taking appropriate breaks and exercising to reduce stress."

[1267] The above is a specific embodiment of the present invention, which makes it possible to effectively and efficiently support the health management of workers in a factory environment.

[1268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1269] Step 1: Collecting personal health data

[1270] Users collect health data such as heart rate, steps, calories burned, and sleep time from their smartphones or wearable devices. This data may be entered manually by the user or automatically recorded by the device.

[1271] Input: Health data such as heart rate, steps, calories burned, and sleep time

[1272] Output: Data collected by smartphones and wearable devices

[1273] Step 2: Preprocessing the data

[1274] The device preprocesses the health data sent by the user, including adjusting the resolution of the data, converting voice data to text, and cleaning the text data, so that the data is in a form suitable for analysis on the server.

[1275] Input: Raw data collected

[1276] Output: Preprocessed data

[1277] Step 3: Integrate the data

[1278] The server then integrates the preprocessed data, combining data of different formats (text, audio, images, video, etc.) into a single unified dataset. Specific operations include changing the order in which the data is viewed and standardizing the format.

[1279] Input: Preprocessed data

[1280] Output: A consolidated dataset

[1281] Step 4: AI analysis

[1282] The server analyzes the integrated health data in real time, using a machine learning generative model to assess the user's health status from the data. For example, if the user's heart rate is high, it can determine whether the user is showing signs of dehydration or stress.

[1283] Input: Integrated dataset

[1284] Output: Health status assessment results

[1285] Step 5: Generate a Health Management Plan

[1286] The server then generates a personalized health management action plan based on the analysis results, with specific recommendations such as "eat fruit daily to increase your vitamin intake" or "do yoga three times a week to reduce stress."

[1287] Input: Health status assessment results

[1288] Output: personalized health care plan

[1289] Step 6: Notify users

[1290] The server then sends the generated health management plan to the user's smartphone or wearable device, which then sends a push notification or alert.

[1291] Enter: personalized health care plans.

[1292] Output: Plan notified to user's device

[1293] Step 7: Gather feedback

[1294] The user inputs the results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and is used to generate future plans.

[1295] Input: User feedback

[1296] Output: Data used to generate improved plans

[1297] The above are the specific processing steps of the system program of the present invention.

[1298] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1299] The present invention combines a system that centrally collects, pre-processes, integrates, and analyzes personal health data in real time to provide a customized health management plan, with an emotion engine that recognizes the user's emotions. Specific embodiments of the present invention will be described in detail below.

[1300] 1. Collection of User Data

[1301] Users use smartphones or wearable devices to input or collect daily health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of their daily meals into a smartphone app and measure their steps and heart rate with a smartwatch.

[1302] 2. Data Preprocessing

[1303] The device preprocesses the health data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes their contents to extract important indicators such as sleep duration and exercise volume.

[1304] 3. Data integration

[1305] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[1306] 4. AI-powered analysis

[1307] The server analyzes the integrated health data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it may determine that the user is lacking exercise based on data from the past week.

[1308] 5. Generate a health management plan

[1309] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[1310] 6. Emotion Recognition by Emotion Engine

[1311] The server uses the voice and image data provided by the user to recognize the user's emotions using an emotion engine. Specifically, it analyzes voice tone and facial expressions to estimate emotional states such as stress, happiness, and fatigue. For example, it can analyze the user's voice tone and choice of words from their voice memo to estimate that their stress level is high.

[1312] 7. Adjust your plan based on your emotional state

[1313] The server adjusts the generated health management plan based on the user's emotional state as recognized by the emotion engine: for example, if the user is feeling stressed, relaxation and stress relief techniques are added to the plan.

[1314] 8. Notice to Users

[1315] The server notifies the user of the generated and adjusted health management plan via their smartphone or wearable device. The notification format is adjusted according to the user's preferences and the characteristics of the device. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes and have a relaxing massage in the evening."

[1316] 9. Collecting Feedback

[1317] Users can input feedback about the results and impressions of the health management plan they have implemented. This feedback is sent to the server and used to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[1318] Specific examples

[1319] For example, if a user records their diet on a smartphone app and measures their exercise volume with a wearable device, these data are preprocessed on the device and sent to a server. Furthermore, if the user adds a voice memo to the app, the emotion engine analyzes the voice and recognizes that the user is feeling stressed. The server integrates these data, evaluates the user's health condition using an AI model, and, taking into account the results of the emotion engine, generates a specific health management plan, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, which will further personalize future plans.

[1320] The above is a specific embodiment of the present invention. The present invention effectively and efficiently supports the user's health management and also makes it possible to provide an optimal plan according to the user's emotional state.

[1321] The processing flow will be explained below.

[1322] Step 1:

[1323] Users input or collect daily health data using smartphones or wearable devices. This data includes dietary information entered into a smartphone app, steps taken and heart rate measured by a smartwatch, and voice memos recording impressions and physical condition.

[1324] Step 2:

[1325] The device preprocesses various data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also cleans the text data and removes unnecessary information. For example, it transcribes voice memos and extracts health-related keywords from their content.

[1326] Step 3:

[1327] The server centrally integrates the preprocessed data. Data collected from different devices is aggregated for each user and managed as a single dataset. For example, food logs from a smartphone, step count data from a smartwatch, and gratitude keywords extracted from voice memos can all be aggregated.

[1328] Step 4:

[1329] The server analyzes the integrated health data, using generative models and machine learning algorithms to assess the user's health status, for example, by analyzing the amount of exercise, diet, and sleep patterns over the past week to determine whether the user is physically inactive.

[1330] Step 5:

[1331] The server uses an emotion engine to recognize the user's emotional state based on the user's voice data and image data. For example, it analyzes the tone of the voice memo and facial expressions in the image to determine whether the user is feeling stressed.

[1332] Step 6:

[1333] The server generates a customized health management plan based on the analysis results and the user's emotional state. The plan includes recommended actions to improve the user's health, such as diet, exercise, and sleep. For example, a user who is feeling stressed due to lack of exercise might be advised to "walk 30 minutes every day and do yoga three times a week."

[1334] Step 7:

[1335] The server notifies the user of the generated health management plan. The user can check the plan through push notifications on the smartphone app, emails, alerts on wearable devices, etc. For example, a notification on the smartphone may read, "Today's health management plan: 30 minutes of walking and a relaxing massage in the evening."

[1336] Step 8:

[1337] The user carries out the health management plan they receive. They then input their results and impressions as feedback into the smartphone app. For example, they might record things like, "I walked for 30 minutes today and also had a relaxing massage," or "I felt relaxed after the massage."

[1338] Step 9:

[1339] The server collects user feedback and stores it in a database. Based on this feedback, the next health plan can be further personalized, and the user's health status can be continuously monitored and improved. For example, based on the feedback, the next plan may adjust the walking time or suggest different relaxation methods.

[1340] The above processing steps realize a system that comprehensively manages a user's health data and provides an optimal health management plan that takes into account the user's emotional state.

[1341] Example 2

[1342] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1343] Modern health management systems are capable of collecting and centrally managing personal health data. However, these systems often lack the ability to analyze data in real time and generate personalized health management plans. Furthermore, they lack the ability to provide health management plans that take into account the user's emotional state. Therefore, there is a need for a system that can collect, preprocess, integrate, and analyze personal health and emotional data in real time and provide customized health management plans that take into account the user's emotional state.

[1344] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1345] In this invention, the server includes means for centrally collecting personal health data, means for preprocessing the health data, means for integrating the preprocessed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for notifying the user of the health management plan, means for collecting and analyzing emotional data of the user, and means for adjusting the health management plan based on the emotional data. This makes it possible to monitor the health status of an individual in detail in real time and provide an optimal health management plan according to the emotional state.

[1346] "Means for collection" refers to the method or device that allows users to input daily health data using a smartphone or wearable device and have it imported into the system.

[1347] "Preprocessing means" refers to methods or devices that organize and transform collected health data into a format suitable for analysis, such as standardizing the resolution of image data or converting audio data into text.

[1348] The "integrating means" refers to a method or device for consolidating the preprocessed data, associating it with each user, and storing it in a database.

[1349] "Real-time analysis means" means a method or device for analyzing the integrated data in real time to assess the user's health status, including using generative models or machine learning algorithms.

[1350] A "means for generating a healthcare plan" is a method or device for creating an individualized healthcare plan based on the results of real-time analysis.

[1351] The "means for notifying the user" refers to a method or device for transmitting the generated health management plan to the user's smartphone or wearable device to notify the user.

[1352] "Means for collecting and analyzing emotional data" refers to a method or device for recognizing and analyzing the emotional state of a user using voice data or image data provided by the user.

[1353] "Means for adjusting a healthcare plan based on emotional data" refers to a method or apparatus for further optimizing or modifying an already generated healthcare plan based on a recognized emotional state.

[1354] This invention is a system that centrally collects, preprocesses, integrates, and analyzes personal health and emotional data in real time to generate and provide a customized health management plan. This system also takes into account the user's emotional state, providing more personalized health management.

[1355] User Data Collection

[1356] Users use smartphones or wearable devices to input or collect daily health data. This data includes text diaries, food and exercise logs, voice memos, images, videos, etc. For example, a user might enter the details of their daily meals into a smartphone app and measure their steps and heart rate with a smartwatch.

[1357] Data Preprocessing

[1358] The device preprocesses the health data sent by the user. Specifically, it standardizes the resolution of image data and converts voice data to text. It also parses the text data to remove unnecessary information and extract the necessary information. For example, it converts voice memos into text and analyzes their contents to extract important indicators such as sleep duration and exercise volume.

[1359] Data integration

[1360] The server then consolidates the pre-processed data, creating a centralized database for each user, storing data sent from various devices. For example, food logs from a smartphone and step count data from a smartwatch can be combined into a unified data set.

[1361] AI-powered analysis

[1362] The server analyzes the integrated health data. It uses generative models and machine learning algorithms to assess the user's health status. Specifically, it analyzes the data as input and predicts important health indicators such as stress levels and lack of exercise. For example, it determines that the user is lacking exercise based on data from the past week.

[1363] Generate a health management plan

[1364] The server then generates a customized health management plan based on the analysis results, which includes detailed instructions for diet, exercise, and sleep, such as "walk 30 minutes every day" and "eat foods rich in vitamin D."

[1365] Emotion recognition by emotion engine

[1366] The server uses the voice and image data provided by the user to recognize the user's emotions using an emotion engine. Specifically, it analyzes voice tone and facial expressions to estimate emotional states such as stress, happiness, and fatigue. For example, it can analyze the user's voice tone and choice of words from their voice memo to estimate that their stress level is high.

[1367] Adjusting your plan based on your emotional state

[1368] The server adjusts the generated health management plan based on the user's emotional state as recognized by the emotion engine: for example, if the user is feeling stressed, relaxation and stress relief techniques are added to the plan.

[1369] User Notification

[1370] The server then sends the generated and adjusted health management plan to the user's smartphone or wearable device. The notification format is adjusted according to the user's preferences and the characteristics of the device. For example, a notification may be sent to the smartphone saying, "Today's health management plan: Walk for 30 minutes and have a relaxing massage in the evening."

[1371] Gathering feedback

[1372] Users input their results and impressions of the health management plan they have implemented as feedback. The server receives this feedback and uses it to generate future plans. This allows the system to continuously monitor and improve the user's health condition.

[1373] Specific examples

[1374] For example, if a user records their diet in a smartphone app and measures their exercise volume with a wearable device, these data are preprocessed on the device and sent to a server. Furthermore, if the user adds a voice memo to the app, the emotion engine analyzes the voice and recognizes that the user is feeling stressed. The server integrates these data, evaluates the user's health status using a generative model, and, taking into account the results of the emotion engine, generates a specific health management plan, such as "eat fruit every day to increase vitamin intake" or "do yoga three times a week to reduce stress." This plan is notified to the user via the smartphone app, and the user can enter feedback on the results of actually following the plan, which will further personalize future plans.

[1375] Example prompts to be input to the generative AI model

[1376] "Could you please give me some advice on diet management? My current diet consists of oatmeal for breakfast, salad for lunch, and pasta for dinner."

[1377] "I feel like I haven't been getting enough exercise lately. Please tell me a specific exercise plan."

[1378] "My stress levels are high. How can I relax?"

[1379] "Generate a health plan for this week. I've already entered my daily steps, meals, and sleep."

[1380] The above is a specific embodiment of the present invention. The present invention effectively and efficiently supports the user's health management and also makes it possible to provide an optimal plan according to the user's emotional state.

[1381] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1382] Step 1:

[1383] Users collect health data using smartphones and wearable devices.

[1384] Specific operation: The user enters the details of their meal into a smartphone app, and the wearable device measures their exercise volume and heart rate.

[1385] Input: Text data and sensor data on diet, exercise, and sleep.

[1386] Output: Collected health data.

[1387] Step 2:

[1388] The device preprocesses the collected health data.

[1389] Specific operations: Image data is resized to 500x500 pixels, voice memos are converted to text using voice recognition software, and the text data is analyzed to extract key information.

[1390] Input: Collected health data (text, images, audio, etc.).

[1391] Output: Preprocessed health data (standardized images, transcribed audio, parsed text).

[1392] Step 3:

[1393] The server consolidates the pre-processed data.

[1394] Specific operation: Meal data from the smartphone and exercise data from the wearable device are associated with the user ID and stored in a centralized database.

[1395] Input: Preprocessed health data.

[1396] Output: Integrated health database.

[1397] Step 4:

[1398] The server analyzes the integrated data in real time using generative AI models.

[1399] What it does: Input the integrated data into an analytical model to calculate stress levels and physical inactivity scores.

[1400] Input: Integrated Health Database.

[1401] Output: User's health assessment (stress level, physical inactivity score, etc.).

[1402] Step 5:

[1403] The server generates a customized health care plan based on the analysis results.

[1404] Specific actions: Based on the analysis results, specific instructions such as "walk 30 minutes every day" and "eat foods rich in vitamin D" are created.

[1405] Input: User's health assessment.

[1406] Output: A customized health care plan.

[1407] Step 6:

[1408] The server collects and analyzes the emotion data.

[1409] Specific operation: Using voice and image data provided by the user, the system analyzes voice tone and facial expressions to recognize the user's emotional state.

[1410] Input: Audio data, image data.

[1411] Output: The user's emotional state (stress, happiness, fatigue, etc.).

[1412] Step 7:

[1413] The server adjusts the health management plan based on the emotion data.

[1414] Specific behavior: If the user's stress level is high, add relaxation and stress relief actions to the health management plan.

[1415] Input: User's emotional state, customized health care plan.

[1416] Output: An adjusted health care plan that takes into account your emotional state.

[1417] Step 8:

[1418] The server notifies the user of the generated and adjusted health care plan.

[1419] Specific operation: A push notification is sent to the smartphone, informing the user of the health management plan for today: 30 minutes of walking and a relaxing massage in the evening.

[1420] Enter: Coordinated Health Care Plan.

[1421] Output: A notification message to the user.

[1422] Step 9:

[1423] The user inputs the results and impressions of the health management plan they have implemented as feedback. The server receives this feedback and reflects it in the generation of subsequent plans.

[1424] Specific operation: The user writes in the smartphone app, "I walked for 30 minutes today" and "I felt good," and the server reflects this information in the next plan.

[1425] Input: User feedback.

[1426] Output: Feedback data reflected in future plans.

[1427] (Application example 2)

[1428] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1429] In recent years, personal health management has become increasingly important, with a particular need for real-time monitoring of health status and appropriate feedback. However, conventional systems have limitations in collecting and analyzing health data and providing personalized health plans, making it difficult to simultaneously manage workers' health and emotional states, particularly in the workplace. Furthermore, health management plans are not appropriately adjusted according to work conditions and emotions, making it difficult to achieve sufficient results in improving work efficiency and maintaining health.

[1430] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for centrally collecting personal health data, means for pre-processing the health data, means for integrating the pre-processed data, means for analyzing the integrated data in real time, means for generating a customized health management plan, means for collecting emotional data, means for analyzing the emotional data, means for adjusting the health management plan based on the analyzed emotional data, and means for notifying the user of the health management plan. This makes it possible to simultaneously manage the health state and emotional state of a worker and propose optimal work plans or break times in real time.

[1431] "Personal health data" refers to information such as heart rate, steps taken, and exercise volume collected through devices such as smartwatches and smartphones.

[1432] "Preprocessing" refers to the process of extracting necessary information from collected raw data using techniques such as syntax analysis and standardization, thereby improving the quality of the data.

[1433] "Integration" refers to aggregating preprocessed data into a centralized database such as a cloud server and managing it as a unified data set.

[1434] "Real-time analysis" refers to the immediate assessment of the current state based on collected and integrated data, including analysis using generative models and machine learning algorithms.

[1435] A "customized health management plan" refers to a plan that provides specific advice on optimal diet, exercise, rest, etc. based on an individual's health and emotional data.

[1436] "Emotional data" refers to information about a user's emotional state analyzed by the emotion engine using voice, facial expressions, etc.

[1437] An "emotion engine" is a system that analyzes voice tone and facial expressions to estimate a user's emotional state, such as stress, happiness, or fatigue.

[1438] "Adjusting a health management plan" refers to optimizing and modifying an existing health management plan based on the analyzed emotional data.

[1439] "Notification" refers to the act of informing the user of a generated or adjusted health management plan by sending a notification to a smartphone or wearable device.

[1440] "Suggesting work plans or break times" means providing workers with optimal work progress methods and appropriate break times based on health and emotional data.

[1441] The present invention relates to a system that manages and analyzes the health and emotional state of factory workers in real time and provides optimal work plans and break times.

[1442] Health data collection

[1443] Users use smartwatches or smartphones to collect and input daily health data such as heart rate, steps taken, and exercise volume, including data automatically collected through wearable devices.

[1444] Collecting Emotional Data

[1445] The device (e.g., a robot in a factory) collects the voice and facial expressions of the worker using CCTV cameras and audio microphones, and then uses an emotion engine to recognize the user's emotional state and estimate their state of stress, happiness, fatigue, etc. in real time.

[1446] Data Preprocessing

[1447] The collected health and emotion data is preprocessed by the device's on-board computer, which converts voice data into text, standardizes the resolution of image data, and removes unnecessary information through syntactic analysis to extract key indicators.

[1448] Data integration

[1449] The pre-processed data is sent to a cloud server and integrated into a centralized database. By centrally managing health and emotion data for each user, subsequent analysis processes can be carried out efficiently.

[1450] AI-powered analysis

[1451] The server analyzes the integrated data in real time using AI models (e.g., generative models such as TensorFlow or PyTorch) to assess the health and emotional state of workers and predict key health indicators such as stress levels and lack of exercise.

[1452] Generate a health management plan

[1453] The server generates a customized health management plan based on the analysis results. This plan takes into account the worker's health and emotional state and provides specific recommendations for optimal work schedules and break times. For example, it may include specific instructions such as "Take 10 minutes of relaxation time" or "Take a 5-minute break for deep breathing."

[1454] User Notification

[1455] The generated health management plan is sent from the server to the user via their device, such as a smartphone or wearable device, in real time.

[1456] Gathering feedback

[1457] Users input their results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and used to generate future plans. The system continuously monitors the user's health and emotional state and supports improvement.

[1458] Specific examples

[1459] For example, heart rate and step count data collected by the user's smartwatch is sent to a cloud server, while the robot's CCTV camera analyzes the worker's facial expressions in real time to assess their stress level. As a result, the server generates a health management plan such as "Take 10 minutes to relax," and notifies the worker via their smartphone.

[1460] Prompt Sentence Examples

[1461] "Recommend optimal break plans based on workers' health and emotional data. Health data includes heart rate and number of steps. Emotion data includes emotion labels based on facial expressions."

[1462] The present invention makes it possible to simultaneously manage the health and emotional state of factory workers and provide appropriate feedback in real time, thereby effectively supporting work efficiency and worker health.

[1463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1464] Step 1:

[1465] Users collect and input health data using a smartwatch or smartphone. This data includes heart rate, number of steps, and amount of exercise. The collected data is sent from the wearable device to the smartphone. Input: Heart rate, number of steps, amount of exercise. Output: Health data transferred to the device.

[1466] Step 2:

[1467] The terminal collects the voices and facial expressions of workers using CCTV cameras and audio microphones. This includes collecting voice data and image data in real time. Input: Voice data, facial expression data. Output: Emotion data collected in real time.

[1468] Step 3:

[1469] The device preprocesses the collected health and emotion data. Voice data is converted to text (for example, using voice recognition software), and image data has its resolution standardized. It also removes unnecessary information from the collected data and extracts important information. Input: Voice data, image data. Output: Preprocessed health and emotion data.

[1470] Step 4:

[1471] The preprocessed data is sent from the device to a cloud server and integrated into a centralized database. This allows the data to be organized by user. Input: Preprocessed health data and emotion data. Output: Integrated data on the cloud server.

[1472] Step 5:

[1473] The server analyzes the integrated data in real time using an AI model (e.g., a generative model such as TensorFlow or PyTorch). Health and emotional states are evaluated, and important indicators such as stress levels and lack of exercise are predicted. Input: Integrated data. Output: Analysis results (evaluation of health and emotional states).

[1474] Step 6:

[1475] The server generates a customized health management plan based on the analysis results. This plan includes specific work plans and rest times and is created for real-time feedback. Input: Analysis results. Output: Health management plan.

[1476] Step 7:

[1477] The server notifies the user of the generated health management plan via their smartphone or wearable device. This notification is done in real time, providing the user with optimal advice. Input: Health management plan. Output: Plan notified to the user.

[1478] Step 8:

[1479] The user inputs the results and impressions of the health management plan they have implemented as feedback. This feedback is sent to the server and used to generate future plans. Input: Feedback data. Output: Feedback data sent to the server.

[1480] Step 9:

[1481] The server updates the database and AI model based on the collected feedback, continuously improving the system, which allows it to provide more accurate and personalized plans for the user's health and emotional state. Input: Feedback data. Output: Updated database and AI model.

[1482] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1483] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1484] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1485] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1486] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1487] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1488] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1489] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1490] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1491] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1492] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1493] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1494] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1495] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1496] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1497] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1498] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1499] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1500] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1501] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1502] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1503] The following is further disclosed regarding the above embodiment.

[1504] (Claim 1)

[1505] A means of centrally collecting personal health data;

[1506] means for pre-processing the health data;

[1507] means for integrating the pre-processed data;

[1508] means for analyzing the integrated data in real time;

[1509] means for generating a customized health care plan;

[1510] The system includes means for informing a user of said health care plan.

[1511] (Claim 2)

[1512] 10. The system of claim 1, wherein the health data includes text data, audio data, image data, and video data.

[1513] (Claim 3)

[1514] 10. The system of claim 1, wherein the means for analyzing in real time includes analysis using a generative model.

[1515] (Claim 4)

[1516] 10. The system of claim 1, further comprising means for collecting user feedback based on the customized health management plan.

[1517] (Claim 5)

[1518] 10. The system of claim 1, wherein the means for collecting health data uses a smartphone or a wearable device.

[1519] "Example 1"

[1520] (Claim 1)

[1521] A means of centrally collecting personal health data;

[1522] means for pre-processing the health data;

[1523] means for integrating the pre-processed data;

[1524] means for analyzing the integrated data in real time;

[1525] means for generating a customized health care plan;

[1526] means for notifying a user of the health management plan;

[1527] The system includes a means for collecting feedback from users and incorporating said feedback into the next plan.

[1528] (Claim 2)

[1529] 10. The system of claim 1, wherein the health data includes text data, audio data, image data, and video data.

[1530] (Claim 3)

[1531] 10. The system of claim 1, wherein the means for analyzing in real time includes analysis using a machine learning algorithm.

[1532] "Application Example 1"

[1533] (Claim 1)

[1534] A means of centralizing data on personal health status;

[1535] means for pre-processing said health condition data;

[1536] means for integrating the pre-processed data;

[1537] means for instantly analyzing the integrated data;

[1538] a means for generating an individualized health management action plan;

[1539] means for notifying a user of the health management action plan;

[1540] A means of monitoring worker health in real time in a factory environment;

[1541] means for transmitting the monitoring results to a server so that an administrator can check them;

[1542] A system including:

[1543] (Claim 2)

[1544] 10. The system of claim 1, wherein the health data includes text data, audio data, image data, and video data.

[1545] (Claim 3)

[1546] 10. The system of claim 1, wherein the means for instantly analyzing includes analysis using a generative model.

[1547] "Example 2: Combining Emotion Engines"

[1548] (Claim 1)

[1549] A means of centrally collecting personal health data;

[1550] means for pre-processing the health data;

[1551] means for integrating the pre-processed data;

[1552] means for analyzing the integrated data in real time;

[1553] means for generating a customized health care plan;

[1554] means for notifying a user of the health management plan;

[1555] means for collecting and analyzing user emotion data;

[1556] The system includes means for adjusting a health care plan based on said emotional data.

[1557] (Claim 2)

[1558] 10. The system of claim 1, wherein the health data and emotion data includes text data, audio data, image data, and video data.

[1559] (Claim 3)

[1560] 10. The system of claim 1, wherein the means for analyzing in real time includes analysis using a generative model.

[1561] "Application example 2 when combining emotion engines"

[1562] (Claim 1)

[1563] A means of centrally collecting personal health data;

[1564] means for pre-processing the health data;

[1565] means for integrating the pre-processed data;

[1566] means for analyzing the integrated data in real time;

[1567] means for generating a customized health care plan;

[1568] means for notifying a user of the health management plan;

[1569] a means for collecting emotion data;

[1570] means for analyzing the emotion data;

[1571] means for adjusting a health care plan based on said analyzed emotional data;

[1572] A system including:

[1573] (Claim 2)

[1574] 10. The system of claim 1, wherein the health data includes text data, audio data, image data, and video data.

[1575] (Claim 3)

[1576] 10. The system of claim 1, wherein the means for analyzing in real time includes analysis using a generative model.

[1577] (Claim 4)

[1578] The system according to claim 1, wherein the notification means includes means for suggesting an optimal work plan or break time in real time. [Explanation of symbols]

[1579] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of centrally collecting personal health data; means for pre-processing the health data; means for integrating the pre-processed data; means for analyzing the integrated data in real time; means for generating a customized health care plan; The system includes means for informing a user of said health care plan.

2. The system of claim 1 , wherein the health data includes text data, audio data, image data, and video data.

3. The system of claim 1 , wherein the means for analyzing in real time includes analysis using a generative model.

4. The system of claim 1 , further comprising means for collecting user feedback based on the customized health management plan.

5. The system of claim 1 , wherein the means for collecting health data uses a smartphone or a wearable device.

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